Regional Residential Heating Oil Model

*Draft April 13, 2005*

Table of Contents

[1. Overview of Regional Residential Heating Oil Model
2](#overview-of-regional-residential-heating-oil-model)

[2. Data Sources 4](#data-sources)

[3. Residential Heating Oil Demand 5](#residential-heating-oil-demand)

[A. Introduction 5](#a.-introduction)

[B. Regional Demand Equations 7](#b.-regional-demand-equations)

[1. Residential Heating Oil Price 7](#residential-heating-oil-price)

[2. Weather 8](#weather)

[3. Number of Homes with Heating Oil for Space Heating
11](#__RefHeading___Toc101145791)

[4. Dummy Variables 14](#dummy-variables)

[4. Heating Oil Prices 15](#heating-oil-prices)

[A. Introduction 15](#a.-introduction-1)

[B. Distillate Wholesale Price 16](#b.-distillate-wholesale-price)

[1. Crude Oil Price Change 16](#crude-oil-price-change)

[2. Inventories 17](#inventories)

[3. Total U.S. Distillate Demand 17](#total-u.s.-distillate-demand)

[5. Dummy Variables 17](#dummy-variables-1)

[C. Residential Heating Oil Prices Before State Taxes
19](#c.-residential-heating-oil-prices-before-state-taxes)

[1. Wholesale Distillate Price Change
19](#wholesale-distillate-price-change)

[2. Weather 20](#weather-1)

[3. Inventories 20](#inventories-1)

[4. Dummy Variables 21](#dummy-variables-2)

[D. Residential Heating Oil Prices After State Taxes
22](#d.-residential-heating-oil-prices-after-state-taxes)

[5. Inventories 24](#inventories-2)

[A. Introduction 24](#a.-introduction-2)

[B. Regional Inventory Equations 24](#b.-regional-inventory-equations)

[1. Distillate to Crude Oil Price Differential
25](#distillate-to-crude-oil-price-differential)

[2. Beginning Stocks 25](#beginning-stocks)

[3. Weather 25](#weather-2)

[4. Dummy Variables 26](#dummy-variables-3)

[Appendix A. Detailed Tables 27](#__RefHeading___Toc101145813)

[Appendix B. Variable Definitions 33](#appendix-b.-variable-definitions)

[Appendix C. EViews Model Program File 36](#__RefHeading___Toc101145815)

[Appendix D. Regression Results 37](#__RefHeading___Toc101145816)

[Appendix D. Regression Results 37](#__RefHeading___Toc101145817)

# 1. Overview of Regional Residential Heating Oil Model

Distillate fuel oil is consumed in several different sectors, including
on-highway transportation, residential, commercial, industrial, and
agricultural (Figure 1). Other applications include off-highway diesel,
railroad, vessel bunkering, electric power, and oil company use.

![](media/image1.wmf)

Distillate fuel oil is a general classification for one of the petroleum
fractions produced in crude oil distillation operations. First,
distillate fuel is classified as No. 1, No. 2, or No. 4 fuel oil where
the higher number denotes a heavier or more viscous liquid. Heating oil
used in the residential sector is primarily No. 2 distillate fuel oil
(about 1.5 percent is No. 1 fuel oil). Second, distillate fuel is
classified as diesel fuel or fuel oil. The most significant distinction
between diesel fuel and fuel oil is that diesel fuel has a maximum
allowed sulfur level of 500 parts per million or lower while fuel oil
has a 5,000 parts per million maximum. Consequently, diesel fuel can be
used as fuel oil but fuel oil generally cannot be used as diesel fuel.
Heating oil used in the residential sector is fuel oil that has the
higher sulfur limit.

Heating oil ranks as the third most important source of residential
energy in the Nation, with nearly 8 percent of all households using
heating oil as their primary space heating fuel (Energy Information
Administration, *2001 Residential Energy Consumption Survey*, Table
HC1-9a). Heating oil is also used by households for water heating.

The objective of the regional short-term residential heating oil model
is to generate residential price forecasts for the four census
districts: Northeast, South, Midwest, and West (see Appendix A1 for
map). Regional residential heating oil prices are estimated as a
function of the wholesale distillate fuel price, regional stocks, and
weather (Figure 2). Regional residential heating oil prices are then
aggregated to the U.S. level by weighting regional prices by estimated
regional demands.

![](media/image2.wmf){width="5.993055555555555in"
height="4.370833333333334in"}

# 2. Data Sources

The monthly volume and price data used in the regional heating oil model
appear in two EIA publications: the Petroleum Supply Monthly (PSM) and
Petroleum Marketing Monthly (PMM) (Figure 3). Weekly regional inventory
data published in the Weekly Petroleum Status Report (WPSR) are used for
the most recent two months where monthly data is not yet available. A
monthly time series of the number of homes with heating oil as the main
space heating fuel is derived by linear interpolation from the
quadrennial Residential Energy Consumption Survey (RECS).

![](media/image3.wmf)

The PSM includes volume data from surveys of primary suppliers such as
refineries, pipelines, and bulk terminals. The PMM includes volume and
price data from the primary suppliers as well as other wholesale and
retail suppliers. Because PSM surveys do not classify products by end
use it is not possible to produce a complete regional supply and demand
balance for residential heating oil. A U.S. supply-demand balance for
distillate fuel is produced separately in the Short-Term Energy Outlook
model.

# 3. Residential Heating Oil Demand

## A. Introduction

Heating oil has two primary uses in the residential sector: space
heating and water heating. Space heating makes up about 81% of total
residential heating oil demand (Figure 4).

![](media/image4.wmf)

Heating oil has the largest share of the space heating fuel market in
the Northeast with 6.24 million homes representing almost 31 percent of
all homes in the region using heating oil as their primary space heating
fuel and an additional 0.18 million that use heating oil as a secondary
fuel (Figure 5). Market shares in the other regions are much smaller,
ranging from 3.2 percent in the Midwest down to 0.7 percent in the West.

![](media/image5.wmf)

Heating oil consumption per household is greatest for space heating
(Figure 6).

![](media/image6.wmf)

Average total annual average residential sector heating oil consumption
is greatest in the Northeast because of the greater number of homes
using heating oil for space heating and colder weather (Figure 7).

![](media/image7.wmf)

## B. Regional Demand Equations

The Regional Short-Term Energy Model for residential heating oil
includes residential sector heating oil demand for the four census
regions. Estimates of regional residential heating oil demand are used
to weight regional heating oil prices to calculate an average U.S.
residential heating oil price.

The census region residential heating oil demand equations include the
following four groups of explanatory variables:

  -------------------------- --------------------------------------------
  **Residential Heating Oil  The average real residential heating oil
  Price**                    price in the census region.

  **Weather**                Census division heating degree day
                             deviations from normal weighted to the
                             census region level based on the number of
                             homes in each census division that use
                             heating oil as their primary space heating
                             fuel.

  **Number of Homes**        The number of homes in each census region
                             that use heating oil as their primary space
                             heating fuel.

  **Dummy Variables**        Dummy variables are included to capture the
                             seasonality in heating oil consumption and
                             to eliminate the effects of certain events
                             that are considered outliers in the data
                             series.
  -------------------------- --------------------------------------------

### 1. Residential Heating Oil Price

The average real census region residential heating oil price (nominal
price divided by the consumer price index) is included to capture the
expected small negative elasticity of demand with respect to price.

Because heating oil demand is highly seasonal we include two price
variables, one for November through March and one for April through
October. A given change in price is expected to have a larger affect on
demand in the winter than in the summer. For example, the Northeast
region heating oil demand model includes the following two independent
variables:

(d2rcune / cicpius) \* (nov+dec+jan+feb+mar)

(d2rcune / cicpius) \* (apr+may+jun+jul+aug+sep+oct)

where,

> d2rcune = Northeast residential heating oil price
>
> cicpius = Consumer price index

The regression results generally confirm that negative relationships
between demand and price are present in all four census regions and the
price effect is larger in the winter than in the summer (Table 1).
Residential demand declines as the real residential heating oil price in
each census region rises with all other variables held constant.
Exceptions are the Northeast during the summer months and the West
during the winter (although these results are not statistically
significant).

We can calculate an approximate price elasticity of demand based on the
estimated regression coefficients in the residential price equations
using the average residential demands and prices over the regression
equation estimation period (Jan. 1995 -- Dec. 2004), which are reported
in Table 1. For example, the average winter residential demand and price
for the Northeast region were 442 thousand barrels per day and 111.9
cents per gallon, respectively. An estimated price coefficient of
-0.002172 (with heating oil demand in millions of barrels per day)
implies a 10 percent increase in price, from 111.9 to 123.1 cents per
gallon, reduces residential demand from 442 to 418 thousand barrels per
day, or 5.5 percent. This price elasticity of demand is -0.55 (the 5.5
percent reduction in demand divided by the 10% increase in price).

+--------------------------+----------+---------+--------+---+----------+
| **Table 1. Calculated    |          |         |        |   |          |
| elasticities of          |          |         |        |   |          |
| residential heating oil  |          |         |        |   |          |
| demand with respect to   |          |         |        |   |          |
| residential heating oil  |          |         |        |   |          |
| price by census          |          |         |        |   |          |
| region.**                |          |         |        |   |          |
+--------------------------+----------+---------+--------+---+----------+
|                          | N        | South   | M      | W |          |
|                          | ortheast |         | idwest | e |          |
|                          |          |         |        | s |          |
|                          |          |         |        | t |          |
+--------------------------+----------+---------+--------+---+----------+
| Winter (November --      |          |         |        |   |          |
| March):                  |          |         |        |   |          |
+--------------------------+----------+---------+--------+---+----------+
| Average demand, million  | 0.442    | 0.068   | 0.055  |   | 0.014    |
| bpd                      |          |         |        |   |          |
+--------------------------+----------+---------+--------+---+----------+
| Average price, cents/gal | 111.9    | 111.8   | 101.7  |   | 112.4    |
+--------------------------+----------+---------+--------+---+----------+
| Estimated coefficient    | -        | -0      | -0.    |   | +0.      |
|                          | 0.002172 | .000302 | 000443 |   | 000019\* |
+--------------------------+----------+---------+--------+---+----------+
| Calculated price         | -0.55    | -0.50   | -0.82  |   | +0.15    |
| elasticity               |          |         |        |   |          |
+--------------------------+----------+---------+--------+---+----------+
| Summer (April --         |          |         |        |   |          |
| October):                |          |         |        |   |          |
+--------------------------+----------+---------+--------+---+----------+
| Average demand, million  | 0.132    | 0.021   | 0.021  |   | 0.005    |
| bpd                      |          |         |        |   |          |
+--------------------------+----------+---------+--------+---+----------+
| Average price, cents/gal | 107.1    | 105.6   | 100.7  |   | 116.1    |
+--------------------------+----------+---------+--------+---+----------+
| Estimated coefficient    | +0.      | -0.0    | -0.    |   | -        |
|                          | 000039\* | 00054\* | 000095 |   | 0.000036 |
+--------------------------+----------+---------+--------+---+----------+
| Calculated price         | +0.03    | -0.27   | -0.45  |   | -0.83    |
| elasticity               |          |         |        |   |          |
+--------------------------+----------+---------+--------+---+----------+
| Elasticities are         |          |         |        |   |          |
| calculated based on      |          |         |        |   |          |
| simple demand and price  |          |         |        |   |          |
| averages over the        |          |         |        |   |          |
| regression equation      |          |         |        |   |          |
| estimation period (Jan.  |          |         |        |   |          |
| 1995 -- Dec. 2004).      |          |         |        |   |          |
|                          |          |         |        |   |          |
| \* Estimated coefficient |          |         |        |   |          |
| not statistically        |          |         |        |   |          |
| significant at the 90%   |          |         |        |   |          |
| confidence interval.     |          |         |        |   |          |
+--------------------------+----------+---------+--------+---+----------+

### 2. Weather

Heating oil demand in the residential sector is highly seasonal because
of the weather-related space heating demand. Fuel consumption for space
heating rises during the winter months and falls during the spring and
summer.

Heating degree days is a useful indicator of the energy required for
space heating. When the daily mean temperature is below 65 degrees F,
most buildings require heat to maintain inside temperatures of 70
degrees. Heating degree days are calculated by subtracting the daily
mean temperature (the average of the day's high and low temperatures)
from 65 degrees F. Each degree below 65 degrees is one degree day. The
daily totals are summed for each month.

Heating degree days for each census region are calculated by weighting
the heating degree days for each census district within the region by
the number of homes with heating oil as the primary space heating fuel
in each district. An example calculation of the heating oil-weighted
heating degree days for the Northeast region is shown in Table 2.
Heating degrees days by month for each census region are shown in Figure
8.

  --------------- --------------- --- --------------------- --- -----------------------
  **Table 2.                                                    
  Calculation of                                                
  Northeast                                                     
  Census Region                                                 
  Heating Degree                                                
  Days, January                                                 
  2004.**                                                       

  Census District Heating degree      Number of homes with      
                  days                heating oil space         
                                      heat                      

  New England     1,474           x   2.700                 =   3,989.8

  Middle Atlantic 1,348           x   [3.572]{.underline}   =   [4,815.1]{.underline}

                                      6.272                     8,794.9

  Divided by                                                ÷   6.272
  total number of                                               
  homes                                                         

  Northeast                                                 =   1,402
  census region                                                 
  heating degree                                                
  days                                                          
  --------------- --------------- --- --------------------- --- -----------------------

![](media/image8.wmf)

Because space heating represents the largest share of residential
heating oil demand, total residential heating oil demand is also
strongly seasonal as shown in Figure 9. The Northeast region with more
homes using heating oil for space heating exhibited the greatest
seasonality in demand in 2003 with monthly demand ranging from a low of
85 thousand barrels per day in July to a high of 655 thousand barrels
per day in January.

![](media/image9.wmf)

We can also illustrate the cold weather impact on demand by comparing
per household demand with heating degree days as shown in Figures 10A
through 10D. One additional heating degree day in the census regions
increases per household daily demand between 0.0013 barrels (0.05
gallons) to 0.0033 barrels (0.14 gallons).

![](media/image10.wmf)![](media/image11.wmf)

![](media/image12.wmf)![](media/image13.wmf)

The residential heating oil demand equations include heating degree day
deviations from normal for both the current month and the previous
month. For example, some of the residential demand impact of a cold
January may not show up until February as residences get their heating
oil tanks refilled. The regression results indicate that in the
Northeast, South and Midwest regions about 81 percent of the demand
impact occurs immediately while 89 percent of the cold weather impact on
demand in the West occurs during the first month.

A rule-of-thumb is that a 10 percent increase in heating degree days
increases fuel required for space heating by 10 percent. The effects of
a 10 percent increase in heating degree days for 2005 on residential
heating oil demand based on the estimated regression equations are
reported in Table 3.

+-----------+-----------+-----------+-----------+-----------+-----------+
| **Table   |           |           |           |           |           |
| 3.        |           |           |           |           |           |
| Estimated |           |           |           |           |           |
| effect of |           |           |           |           |           |
| cold      |           |           |           |           |           |
| weather   |           |           |           |           |           |
| on        |           |           |           |           |           |
| re        |           |           |           |           |           |
| sidential |           |           |           |           |           |
| heating   |           |           |           |           |           |
| oil       |           |           |           |           |           |
| demand by |           |           |           |           |           |
| census    |           |           |           |           |           |
| region,   |           |           |           |           |           |
| 2005.**   |           |           |           |           |           |
|           |           |           |           |           |           |
| (percent  |           |           |           |           |           |
| change in |           |           |           |           |           |
| re        |           |           |           |           |           |
| sidential |           |           |           |           |           |
| sector    |           |           |           |           |           |
| demand)   |           |           |           |           |           |
+-----------+-----------+-----------+-----------+-----------+-----------+
|           | Northeast | South     | Midwest   | West      | U.S.      |
+-----------+-----------+-----------+-----------+-----------+-----------+
| Jan       | 10.5 %    | 7.6 %     | 13.4 %    | 2.4 %     | 10.2 %    |
+-----------+-----------+-----------+-----------+-----------+-----------+
| Feb       | 10.6 %    | 7.4 %     | 11.5 %    | 2.6 %     | 10.2 %    |
+-----------+-----------+-----------+-----------+-----------+-----------+
| Mar       | 12.1 %    | 7.8 %     | 12.4 %    | 2.5 %     | 11.4 %    |
+-----------+-----------+-----------+-----------+-----------+-----------+
| Apr       | 11.3 %    | 8.0 %     | 9.8 %     | 3.1 %     | 10.7 %    |
+-----------+-----------+-----------+-----------+-----------+-----------+
| May       | 10.3 %    | 5.6 %     | 7.9 %     | 3.2 %     | 9.5 %     |
+-----------+-----------+-----------+-----------+-----------+-----------+
| Jun       | 4.4 %     | 1.2 %     | 3.0 %     | 3.0 %     | 3.9 %     |
+-----------+-----------+-----------+-----------+-----------+-----------+
| Jul       | 1.1 %     | 0.2 %     | 0.8 %     | 2.1 %     | 1.0 %     |
+-----------+-----------+-----------+-----------+-----------+-----------+
| Aug       | 0.9 %     | 0.1 %     | 0.8 %     | 0.9 %     | 0.8 %     |
+-----------+-----------+-----------+-----------+-----------+-----------+
| Sep       | 3.6 %     | 0.8 %     | 2.2 %     | 1.1 %     | 3.0 %     |
+-----------+-----------+-----------+-----------+-----------+-----------+
| Oct       | 8.8 %     | 4.1 %     | 5.7%      | 1.6 %     | 7.6 %     |
+-----------+-----------+-----------+-----------+-----------+-----------+
| Nov       | 12.3 %    | 8.0 %     | 11.0 %    | 2.4 %     | 11.3 %    |
+-----------+-----------+-----------+-----------+-----------+-----------+
| Dec       | 11.0 %    | 7.5 %     | 11.6 %    | 2.7 %     | 10.4 %    |
+-----------+-----------+-----------+-----------+-----------+-----------+
|           |           |           |           |           |           |
+-----------+-----------+-----------+-----------+-----------+-----------+
| Average   | 9.8 %     | 6.4 %     | 9.2 %     | 2.4 %     | 9.2 %     |
+-----------+-----------+-----------+-----------+-----------+-----------+
| Al        |           |           |           |           |           |
| ternative |           |           |           |           |           |
| cold      |           |           |           |           |           |
| weather   |           |           |           |           |           |
| scenario  |           |           |           |           |           |
| assumes   |           |           |           |           |           |
| 10% more  |           |           |           |           |           |
| heating   |           |           |           |           |           |
| degree    |           |           |           |           |           |
| days in   |           |           |           |           |           |
| each      |           |           |           |           |           |
| census    |           |           |           |           |           |
| region    |           |           |           |           |           |
| beginning |           |           |           |           |           |
| January   |           |           |           |           |           |
| 2005.     |           |           |           |           |           |
+-----------+-----------+-----------+-----------+-----------+-----------+

The results in Table 3 are consistent with the rule-of-thumb for the
Northeast and Midwest. The West census district has a small response to
cold weather but the small sample population yields less reliable
results.

[]{#__RefHeading___Toc101145791 .anchor}**3. Number of Homes with
Heating Oil for Space Heating**

Heating oil demand is expected to be positively related to the number of
the homes that use heating oil as the main heating fuel. The EIA
Residential Energy Consumption Survey (RECS) reports the number of homes
and volume of heating oil consumed by census division for the following
categories of use:

-   Any use

-   Space heating primary fuel

-   Space heating secondary fuel

-   Water heating

The largest volume of use is as the primary fuel for space heating,
which represents about 80 percent of total residential heating oil
demand in the Nation (detailed tables with the number of homes and
heating oil consumption by census region are available in Appendix A).
Heating oil use as the secondary fuel for space heating represents about
2 percent of total residential demand, with water heating making up the
remaining 18 percent of demand.

The regional heating oil residential demand regression equations include
the number of homes that use heating oil as the primary space heating
fuel. The total number of homes with heating oil as the primary space
heating fuel in each census region is calculated by adding the number of
homes in each corresponding census division. For example, the Northeast
census region includes the Middle Atlantic and New England census
divisions:

  ------------------------ ----------------------------------------------
  Total number of homes in = qhfohrecs_mac + qhfohrecs_nec
  Northeast census region  
  with heating oil space   
  heating                  

  ------------------------ ----------------------------------------------

where,

> ghfohrecs_mac = RECS estimate of homes using heating oil as the
> primary heating fuel in the Middle Atlantic census division
>
> qhfohrecs_nec = RECS estimate of homes using heating oil as the
> primary heating fuel in the New England census division

The RECS surveys are conducted every four years (1989, 1993, 1997, and
2001). A monthly time series is created by linear interpolation between
the RECS survey points (Figure 11). A forecast of the number of homes is
created by multiplying the share of total homes reported in the 2001
RECS survey by regional population forecasts published by the U.S.
Census Bureau.

![](media/image14.wmf)

Sources: Linear interpolation and extrapolation of EIA Residential
Energy Consumption Survey data.

The relationship between heating oil demand and the number of houses
with heating oil as the main space heating fuel should be seasonal. In
other words, a doubling of homes using heating oil for space heating
will have a much larger effect on demand in January than in July.
Because of this expected seasonal relationship the number of homes with
heating oil heat is separated into two seasons: one series includes the
number of homes in November through March (0 otherwise) and a second
series includes the number of homes in April through October (0
otherwise). For example, the Northeast region heating oil demand model
includes the following two independent variables:

(qhfohrecs_mac + qhfohrecs_nec) \* (nov+dec+jan+feb+mar)

(qhfohrecs_mac + qhfohrecs_nec) \* (apr+may+jun+jul+aug+sep+oct)

We can see the effect of a change in the number of homes on heating oil
demand by comparing an estimated average seasonal demand per house for
space heating derived from the 2001 RECS with the estimated regression
coefficients for the number of houses per season in Table 4. For
example, the estimated coefficient on the number of houses with heating
oil as the primary space heating fuel for the Northeast implies that
each additional house will increase winter heating oil consumption by
about 0.019 barrels per day per house, which is less than the estimated
average 0.057 barrels per house per day from RECS.

+-------------------------+---------------------+---------------------+
| **Table 4. Residential  |                     |                     |
| heating oil demand and  |                     |                     |
| number of houses with   |                     |                     |
| heating oil space       |                     |                     |
| heating**               |                     |                     |
+-------------------------+---------------------+---------------------+
|                         | **Estimated average | **Estimated         |
|                         | heating oil demand  | coefficient on      |
|                         | as primary space    | number of houses    |
|                         | heating fuel per    | with heating oil as |
|                         | house from RECS,**  | primary space       |
|                         |                     | heating fuel,**     |
|                         | **(barrels per      |                     |
|                         | house per day)**    | **(barrels per      |
|                         |                     | house per day)**    |
+-------------------------+---------------------+---------------------+
| **Northeast:**          |                     |                     |
+-------------------------+---------------------+---------------------+
| April - October         | 0.017               | 0.0009\*            |
+-------------------------+---------------------+---------------------+
| November - March        | 0.057               | 0.019\*             |
+-------------------------+---------------------+---------------------+
| **South:**              |                     |                     |
+-------------------------+---------------------+---------------------+
| April - October         | 0.015               | 0.0004\*            |
+-------------------------+---------------------+---------------------+
| November - March        | 0.050               | \- 0.016            |
+-------------------------+---------------------+---------------------+
| **Midwest:**            |                     |                     |
+-------------------------+---------------------+---------------------+
| April - October         | 0.021               | \- 0.016            |
+-------------------------+---------------------+---------------------+
| November - March        | 0.051               | 0.037               |
+-------------------------+---------------------+---------------------+
| **West:**               |                     |                     |
+-------------------------+---------------------+---------------------+
| April - October         | 0.015               | 0.006               |
+-------------------------+---------------------+---------------------+
| November - March        | 0.038               | 0.012               |
+-------------------------+---------------------+---------------------+
| Notes: Not              |                     |                     |
| statistically           |                     |                     |
| significant at the 90%  |                     |                     |
| confidence level.       |                     |                     |
|                         |                     |                     |
| Estimated average       |                     |                     |
| heating oil demand as   |                     |                     |
| primary space heating   |                     |                     |
| fuel based on the 2001  |                     |                     |
| Residential Energy      |                     |                     |
| Consumption Survey      |                     |                     |
| (RECS) average          |                     |                     |
| household heating oil   |                     |                     |
| demand with a           |                     |                     |
| correction for          |                     |                     |
| seasonality derived     |                     |                     |
| from 5-year averages    |                     |                     |
| (1998-2003) of total    |                     |                     |
| heating oil residential |                     |                     |
| demand. For example,    |                     |                     |
| estimated winter demand |                     |                     |
| = 2001 RECS average     |                     |                     |
| heating oil space       |                     |                     |
| heating demand per      |                     |                     |
| household x average     |                     |                     |
| winter heating oil      |                     |                     |
| demand / average annual |                     |                     |
| heating oil demand.     |                     |                     |
+-------------------------+---------------------+---------------------+

The regression coefficients are directionally consistent with the
estimated average seasonal space-heating demand per house except in the
South. There are two primary characteristics of the number of homes with
heating oil heat data series that reduces their efficiency as
explanatory variables. The first problem is that the Residential Energy
Consumption Survey only occurs every four years. The monthly historical
household data series used in the regional heating oil model are
generated by simple linear interpolation between surveys.

The second problem with RECS household data for heating oil is that the
share of total households with heating oil heat is small. The 95%
confidence interval around the estimated number of homes in a census
region with heating oil space heating ranges from plus or minus 24
percent (Northeast) to 52 percent (West). This combination of low
frequency and low precision of measurement will significantly affect the
reliability of the estimated regression model coefficients.

### 4. Dummy Variables

Dummy variables are included to reflect the seasonality in heating oil
consumption not captured by seasonality in prices and to eliminate the
effects of certain events that are considered outliers in the data
series.

First, we have dummy variables for each individual month except
December. For example, JAN equals 1 for every January in the time series
and is equal to 0 in every other month. These individual monthly dummy
variables are intended to capture normal demand seasonality as
deviations from December demand. In other words, if the coefficient for
JAN is 0.05, then demand in January is expected to be 50,000 barrels per
day higher than in December. The regression results for the monthly
dummy variables generally reveal lower demand in the summer with demand
peaking in January.

The second group of dummy variables control for nonrecurring events. The
EIA monthly residential heating oil demand survey covers only a sample
rather than universe of all retail suppliers. Every few years the sample
is rebased, which can lead to shifts in demand in certain States. The
most significant shift appears to have occurred recently in the
Northeast and Midwest census regions. This apparent shift is captured by
a dummy variables that account for a demand shift in the coldest winter
months:

  ------------------------------------ ----------------------------------
  (D0212 + D03on) \*                   = 1, if December 2002, or
  (nov+dec+jan+feb+mar)                December, January, or February in
                                       2003 and later years,

                                       = 0, otherwise
  ------------------------------------ ----------------------------------

# 4. Heating Oil Prices

## A. Introduction

Residential heating oil prices are variable within and across regions
(Figures 12A -- 12D).

![](media/image15.wmf)![](media/image16.wmf)

![](media/image17.wmf)![](media/image18.wmf)

The heating oil price model begins with an estimate of the average U.S.
distillate wholesale price. This wholesale price is used as a proxy for
the heating oil wholesale price to each regional residential sector.
Residential heating oil prices excluding State taxes are modeled as a
function of the wholesale price and other variables that may affect the
markup of retail prices over the wholesale price.

Wholesale distillate and retail heating oil prices are modeled as price
spreads rather than price levels. For example, the left-hand side
(dependent) variable in the wholesale distillate price equation is the
difference between the average U.S. distillate wholesale price and the
spot price of WTI crude oil (in cents per gallon). For regional retail
prices the dependent variable is the difference between the census
region residential heating oil retail price and the average U.S.
distillate wholesale price.

## B. Distillate Wholesale Price

Since heating oil is a product of crude oil refining we assume the
wholesale price is directly related to the spot price of crude oil.
There are many measures of crude oil prices because of the many
different grades of crude oil. The spot price of West Texas Intermediate
(WTI) was selected as the representative crude oil that is most closely
related to distillate prices.[^1]

The difference between the average U.S. distillate wholesale price and
the spot price of WTI crude oil (converted from dollars per barrel to
cents per gallon) is estimated as a function of the following
explanatory variables plus a first-order autoregressive error correction
term:

  ------------------------- ---------------------------------------------
  **Crude Oil Price         The change in the current month spot West
  Change**                  Texas Intermediate (WTI) crude oil price from
                            the prior month.

  **U.S. Inventories**      Deviation in prior month ending U.S. total
                            primary stocks from the prior four-year
                            average for that month.

  **U.S. Distillate         Deviation in total distillate demand from
  Demand**                  prior three-year average

  **Dummy Variables**       Dummy variables are included to capture
                            seasonality and to eliminate the effects of
                            certain events that are considered outliers
                            in the data series.
  ------------------------- ---------------------------------------------

### 1. Crude Oil Price Change

A change in the crude oil price is expected to feed forward to wholesale
distillate prices, possibly with a short lag. Including the difference
in the current and prior-month crude oil price as an explanatory
variable allows for some delay in the price pass through.

The structure of the estimated regression equation:

D2WHUUS(t) -- 100\*WTIPUUS(t)/42

> = a0 + a1 \* (100/42) \* \[WTIPUUS (t) -- WTIPUUS (t-1)\]\...

where, D2WHUUS(t) = average U.S. wholesale distillate price in month t

WTIPUUS (t) = West Texas Intermediate (WTI) spot price in month t

a0, a1 = estimated regression coefficients

is equivalent to the following equation:

D2WHUUS (t) = a0 + (1 + a1) \* (100/42) \* (WTIPUUS (t)

> -- a1 \* (100/42) \* WTIPUUS (t-1)\...

with the coefficients on the current and lagged WTI prices constrained
to add to 1.0.

The estimated negative coefficient for *a1* of -0.18 suggests that about
82 percent of a crude oil price increase shows up in the current month
with the remaining 18 percent in the following month.

### 2. Inventories

The beginning of month (end of prior month) total U.S. distillate
inventory as a deviation from the prior four year average for that month
is included as a right-hand side variable. Higher than historical
average U.S. inventory levels are expected to lower the wholesale
distillate price. The estimated regression equation coefficient is
negative as expected. Wholesale distillate prices are up to 2.3 cents
per gallon higher (the greatest price impact occurs during the winter
months) when inventories are 1 standard deviation lower than the last
five year average (2000 -- 2004).

### 3. Total U.S. Distillate Demand

The deviation in total distillate demand from the previous three year
average is a mechanism for recognizing the price impact of demand shocks
such as from cold weather. The estimated coefficient is positive as
expected. Wholesale distillate prices are up to 1.2 cents per gallon
higher (the greatest price impact occurs during the winter months) when
total distillate demand is 1 standard deviation higher than the last
five year average (2000 -- 2004).

### 5. Dummy Variables

Dummy variables are included to reflect the seasonality in wholesale
distillate prices not captured by seasonality in the other variables and
to eliminate the effects of certain events that are considered outliers
in the data series.

First, we have dummy variables for each individual month except
December. For example, JAN equals 1 for every January in the time series
and is equal to 0 in every other month. These individual monthly dummy
variables are intended to capture normal wholesale price seasonality as
deviations from the December price. In other words, if the coefficient
for JAN is 1.00, then the wholesale price in January is expected to be
1.0 cent per gallon higher than in December with all other variables
constant.

The regression results for the monthly dummy variables conform to
expectations with the wholesale price lowest in May and peaks just
before the start of the heating season in September. The observed
seasonality in price provides an incentive for firms to build and hold
heating oil inventories during the summer months for delivery during the
winter heating season.

The second group of dummy variables control for nonrecurring events that
are considered outliers in the data series. These dummy variables shown
in Table 6 identify months when the wholesale price was significantly
higher than expected.

  -------------- ------------- ------------- -----------------------------------
  **Table 6.                                 
  Wholesale                                  
  distillate                                 
  price equation                             
  dummy                                      
  variables.**                               

  Variable       Description   Estimated     Explanation
                               Coefficient   

                                             

  d0001 + d0002  Jan + Feb,    10.7          
                 2000                        

                                             

  d0302 + d0303  Feb + Mar,    8.7           Cold Jan and Feb with natural gas
                 2003                        price spike
  -------------- ------------- ------------- -----------------------------------

## C. Residential Heating Oil Prices Before State Taxes

The Regional Short-Term Energy Model for residential heating oil
includes residential sector heating oil retail prices for the four
census regions. The difference between the census region residential
heating oil retail price and the wholesale distillate price is estimated
as a function of the following explanatory variables plus a first-order
autoregressive error correction term:

  ------------------------- ---------------------------------------------
  **Wholesale Distillate    The change in the current month wholesale
  Price Change**            distillate price from the prior month

  **Regional Weather**      Census division heating degree day deviations
                            from normal weighted to the census region
                            level using the number of homes in each
                            census division that use heating oil as their
                            primary space heating fuel.

  **Regional Inventories**  Deviation in prior end-of-month Petroleum
                            Administration for Defense District (PADD)
                            stocks from the prior four-year average for
                            that month.

  **Dummy Variables**       Dummy variables are included to capture the
                            seasonality in heating oil prices and to
                            eliminate the effects of certain events that
                            are considered outliers in the data series.
  ------------------------- ---------------------------------------------

### 1. Wholesale Distillate Price Change

The residential heating oil price is directly related to the wholesale
price. A change in the wholesale price is expected to feed forward to
residential prices, possibly with a short lag. Including the difference
in the current and prior-month wholesale distillate price as an
explanatory variable allows for some delay in the price pass through.

The structure of the estimated regression equation:

D2RCUxx(t) -- D2WHUUS(t) = a0 + a1 \* \[D2WHUUS(t) -- D2WHUUS(t-1)\]\...

where, D2RCUxx(t) = residential heating oil retail price in region xx in
month t

D2WHUUS(t) = wholesale distillate price in month t

a0, a1 = estimated regression coefficients

is equivalent to the following equation:

D2RCUxx(t) = a0 + (1 + a1) \* (D2WHUUS(t) -- a1 \*D2WHUUS(t-1)\]\...

with the coefficients on the current and lagged wholesale prices
constrained to add to 1.0.

The estimated values of the *a1* coefficients for the different regional
equations suggests that from 47 percent (West) to 63 percent (Midwest)
of a change in the wholesale price shows up in the current month
residential price with the remainder in the following month.

### 

### 2. Weather

Regional heating degree day deviations from normal are included to
capture the possible impact of cold weather on retail prices.
Surprisingly the estimated coefficients on the regional weather variable
for all regions except the South are negative. Colder-than-normal
weather lowers the residential heating oil price with all other
variables held constant. However, none of the weather variables are
statistically significant at the 90 percent confidence interval. The
impact of the negative weather coefficient is small. For example, if
heating degree days are 10% greater than normal, the retail price effect
is less than one-half cent per gallon in the Northeast region.

### 3. Inventories

While total U.S. inventories are included in the wholesale distillate
price regression equation, regional inventory levels are included in the
residential price equations. Inventories are entered as the difference
in beginning-of-month stocks from the prior four year average.

Inventory data are not available by census region. Inventories by
Petroleum Administration for Defense (PAD) District are used for census
regions as shown below. A more detailed description of the
correspondence between census regions and PAD Districts is provided in
Appendix A.

  -------------------------------------- --------------------------------
  **Census Region**                      **PAD District**

  Northeast                              1

  South                                  1

  Midwest                                2

  West                                   5
  -------------------------------------- --------------------------------

The estimated coefficients on inventories are negative as expected only
in the Midwest and West. The greatest impact occurs in the West. When
West region stocks are 1 standard deviation lower than the average over
the last five years (2000 - 2004), residential prices are higher by
about 1.7 cents per gallon. Residential heating oil prices in the South
region would be 0.7 cents lower. The price impact is close to zero in
the Northeast where the estimated coefficient was not statistically
significant.

### 4. Dummy Variables

Dummy variables are again included to reflect the seasonality in
residential heating oil prices (or the markup over the wholesale price)
not captured by seasonality in the wholesale price and inventory
variables and to eliminate the effects of certain events that are
considered outliers in the data series.

The regression results for the monthly dummy variables conform to
expectations with the residential price lowest at the end of summer and
peaking during the winter. The observed seasonality in price reflects
the seasonality in demand and provides an incentive for firms to build
and hold distillate inventories during the summer months for delivery
during the winter heating season.

The second group of dummy variables control for nonrecurring events that
are considered outliers in the data series.

  -------------- --------------- -------------- ------- --------- ------ ----------------
  **Table 8.                                                             
  Residential                                                            
  price equation                                                         
  dummy                                                                  
  variables.**                                                           

                                 Estimated                               
                                 Coefficients                            

  Variable       Description     Northeast      South   Midwest   West   Explanation

                                                                         

  d9612          Dec 1996        n.a.           n.a.    7.7       n.a.   

  d00            Year 2000       7.0            n.a.    n.a.      n.a.   

  d0001          Jan 2000        -6.9           n.a.    n.a.      n.a.   

  d0012          Dec 2000        n.a.           14.5    6.8       10.1   

  d0101          Jan 2001        n.a.           19.2    n.a.      n.a.   

  d01on          Jan 2001 and    12.5           13.3    9.7       17.6   
                 later                                                   

  d0202          Feb 2002        n.a.           n.a.    n.a.      7.8    

  d0303          Mar 2003        9.0            n.a.    n.a.      n.a.   
  -------------- --------------- -------------- ------- --------- ------ ----------------

## D. Residential Heating Oil Prices After State Taxes

The Regional Short-Term Energy Model for residential heating oil
includes residential sector heating oil prices including State taxes for
the four census regions. The heating oil price including State taxes is
calculated by multiplying each region's heating oil price ex tax by a
regional sales tax factor. For example, the Northeast region heating oil
price including State taxes is calculated by:

D2RCANE = D2TXUNE \* D2RCUNE

where,

D2RCANE = Residential heating oil price after taxes, Northeast region

D2TXUNE = Region State sales tax factor (e.g., 1.05 = 5% tax rate)

D2RCUNE = Residential heating oil price before taxes, Northeast region

The regional sales tax factors used in the model are approximations of
the actual average sales tax. We apply the same State tax rates used in
EIA's estimation of State residential petroleum product prices including
taxes published in the annual *State Expenditure and Price Reports*
(<http://www.eia.doe.gov/emeu/states/_price_multistate.html>). The State
tax rate includes the State's portion of the sales tax and excludes any
local options for additional sales tax. The census region tax rate is
calculated by weighting each State's tax rate by the volume of
residential heating oil sales in that State for each month.

We can check the efficacy of this method by comparing the difference
between other published after-tax residential heating oil prices and out
before-tax price. The are two surveys of after-tax residential heating
oil prices:

-   EIA publishes the *State Heating Oil and Propane Program* (SHOPP)
    weekly survey of after-tax residential heating oil prices conducted
    by State energy offices. The SHOPP surveys are conducted only during
    the winter months (beginning of October through mid-March).

-   The U.S. Bureau of Labor Statistics reports residential heating oil
    prices including taxes for the U.S. and census regions as part of
    the monthly Consumer Price Index.

Figure 13 compares the percent differences between the after-tax and
before-tax prices for the SHOPP price and the BLS price. These price
differences are compared with the estimates State tax rate. The
winter-only SHOPP price has averaged 7.7 percent higher than the
before-tax price and the BLS price has averaged 5.6 percent higher than
the before tax price (4.4 percent higher during the same winter months
covered by the SHOPP price) over the last 10 years. The estimated State
tax rate has averaged 4.9 percent (the State tax rate could be as high
as 6.4 percent if the local sales tax option were included at the
maximum level allowed.)

![](media/image19.wmf)

Notes: BLS: Bureau of Labor Statistics, consumer price index survey
after tax residential heating oil price

SHOPP: EIA, *State Heating Oil and Propane Program* after tax
residential heating oil price (winter only).

PMM: Energy information Administration, *Petroleum Marketing Monthly*,
before tax residential heating oil price.

The average difference between the BLS after tax price and the
before-tax price is closer to the estimated State tax rate in the
Northeast. The BLS price averaged 5.0 percent higher than the before-tax
price and the estimated State tax rate averaged 4.9 percent over the
last 10 years. SHOPP prices are not available for census regions.

While the average after- and before-tax heating oil price differences
are very close to the calculated State tax rate, there can be large
deviations in any one month. One fundamental difference between the two
series that leads to the large variations is that the BLS and SHOPP
residential prices are spot (or will-call) price while the before-tax
price represents an actual price paid by households. The actual
household price may be higher or lower than the spot price because many
households lock in winter prices during the summer months. Consequently,
during unexpected price increases during the winter, the average price
actually paid by households may be significantly lower than the spot
price. If spot prices fall then households could pay more. Because the
objective of the Regional Short-Term Energy Model is to forecast average
prices households actually pay we do not attempt to model spot
residential prices. Consequently, some households may pay significantly
more or less than regional prices estimated in this model not only
because of price differences across States and regions but also because
of the market pricing structure.

# 5. Inventories

## A. Introduction

Distillate inventory withdrawals provide the second largest source of
heating oil during the winter heating season. During the peak demand
months of December, January, and February, distillate inventories supply
over 20 percent of total distillate demand on average. Inventories are
built up during the spring and summer months, and typically peak by the
end of September or October and reach their lowest point in March.

Distillate storage consists of three types: primary, secondary, and
tertiary. Primary storage consists of refinery, pipeline, and bulk
terminal stocks. Secondary storage consists primarily of large
above-ground tanks owned by heating oil retail distributors, while
tertiary storage consists mainly of residential and commercial
customers. Inventory survey data are available only at the primary
storage level.

## B. Regional Inventory Equations

The Regional Short-Term Energy Model for residential heating oil
includes inventories for the five Petroleum Administration for Defense
Districts (PADD). Inventory data for census region districts are not
available.

Inventories are modeled as first difference stock changes rather than as
stock levels. The reason for modeling stocks as changes rather than
levels is that this method can provide a smoother transition from the
historical data into the forecast.

The PADD stock change equations include the following explanatory
variables:

  ------------------------- ---------------------------------------------
  **Wholesale Distillate -- Prior month difference between the distillate
  Crude Oil Price           wholesale price and the average acquisition
  Differential**            cost of crude oil to U.S. refineries.

  **Beginning Stocks**      Deviation in prior month ending U.S. total
                            primary stocks from the prior four-year
                            average for that month.

  **Weather**               Census division heating degree days weighted
                            to the census region level using the number
                            of homes in each census division that use
                            heating oil as their primary space heating
                            fuel.

  **Dummy Variables**       Dummy variables are included to capture the
                            seasonality in distillate inventories and to
                            eliminate the effects of certain events that
                            are considered outliers in the data series.
  ------------------------- ---------------------------------------------

### 1. Distillate to Crude Oil Price Differential

An increase in the wholesale distillate price relative to the price of
crude oil is expected have a positive impact on stock changes. Stock
builds should be larger than normal or stock draws smaller with a higher
distillate wholesale price. The higher distillate price should motivate
suppliers to increase output and consumers to reduce demand.

The distillate-to-crude oil price differential is calculated based on
the spot price of West Texas Intermediate (WTI) crude oil. The crude oil
price in dollars per barrel is divided by 42 to convert it to a per
gallon basis.

The estimated coefficients are negative as expected in PADDs 1, 4, and
5, but positive in PADDs 2 and 3. However, the coefficients are
statistically significant (at the 90 percent confidence level) only in
PADD 2.

### 2. Beginning Stocks

The beginning of month (end of prior month) PADD inventory as a
deviation from the prior four year average for that month is included as
a right-hand side variable. Higher than historical average inventory
levels are expected to reduce stock builds or increase stock draws. The
estimated coefficients for all PADDs were negative as expected and
statistically significant in all PADDs except PADD 1. In PADDs 2 through
5 a beginning-of-month inventory that is 1 million barrels above the
previous four-year average reduces the month's normal stock change by
0.3 to 0.4 million barrels.

### 3. Weather

The distillate stock change equations include heating degree day
deviations from normal for selected census divisions as shown below. A
more detailed description of the correspondence between census divisions
and PAD Districts is provided in Appendix A.

  --------------- -------------------------------------------------------
  **PAD           **Census Division**
  District**      

  1               New England, Middle Atlantic, and South Atlantic

  2               East and West North Central

  3               East and West South Central

  4               Mountain

  5               Pacific
  --------------- -------------------------------------------------------

The census division heating degree days are weighted by the number of
households within each division that uses heating oil as the primary
space heating fuel to arrive at the PADD heating degree days.

Weather that is colder than normal should lead to smaller stock builds
or larger stock draws because of the increase in demand, at least in
PADD 1 (the Northeast). Cold weather may not be expected to have
significant effects outside the Northeast because of the small market
shares for space heating. The estimated coefficients are negative as
expected but statistically significant only in PADDs 1 and 2..

### 4. Dummy Variables

Dummy variables are again included to reflect the seasonality in stock
changes not captured by seasonality in weather. The regression results
for the monthly dummy variables conform to expectations that stock
builds occur during the spring and summer months and stock draws during
the winter in PADDs 1, 2, and 3, but not PADDs 4 and 5.

[]{#__RefHeading___Toc101145813 .anchor}**Appendix A. Detailed Tables**

[Appendix A1. Heating Oil Primary Stocks, Residential Consumption, and
Residential Prices by Census Region](#AppendixA1)

[Appendix A2. Correspondence Between Census Divisions and Petroleum
Administration for Defense Districts.](#AppendixA2)

[Appendix A3. Residential Sector Use of Heating Oil, 2001](#AppendixA3)

[Appendix A4. Residential Sector Use of Heating Oil, 1997](#AppendixA4)

[]{#AppendixA1 .anchor}![](media/image20.wmf)

<table style="width:100%;">
<colgroup>
<col style="width: 12%" />
<col style="width: 23%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 8%" />
<col style="width: 18%" />
</colgroup>
<tbody>
<tr class="odd">
<td colspan="10"><span id="AppendixA2"
class="anchor"></span><strong>Appendix A2. Correspondence Between Census
Divisions and Petroleum Administration for Defense
Districts.</strong></td>
</tr>
<tr class="even">
<td rowspan="2"><p><strong>Census</strong></p>
<p><strong>Region</strong></p></td>
<td rowspan="2"><p><strong>Census</strong></p>
<p><strong>Division</strong></p></td>
<td><strong> </strong></td>
<td colspan="5"><strong>Petroleum Administration for Defense District
(PADD)</strong></td>
<td colspan="2"><strong>Households with Heating Oil Space Heating, 2001
(mllions)</strong></td>
</tr>
<tr class="odd">
<td><strong> </strong></td>
<td>1</td>
<td>2</td>
<td>3</td>
<td>4</td>
<td>5</td>
<td><strong>Total</strong></td>
<td><strong>Primary Fuel</strong></td>
</tr>
<tr class="even">
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="odd">
<td>Northeast</td>
<td>New England</td>
<td>CT</td>
<td>X</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td>2.7</td>
<td>2.7</td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td>ME</td>
<td>X</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td> </td>
<td></td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td>MA</td>
<td>X</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td> </td>
<td></td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td>NH</td>
<td>X</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td> </td>
<td></td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td>RI</td>
<td>X</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td> </td>
<td></td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td>VT</td>
<td>X</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td> </td>
<td></td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td> </td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td> </td>
<td></td>
</tr>
<tr class="even">
<td></td>
<td>Middle Atlantic</td>
<td>NJ</td>
<td>X</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td>3.7</td>
<td>3.6</td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td>NY</td>
<td>X</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td> </td>
<td></td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td>PA</td>
<td>X</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td> </td>
<td></td>
</tr>
<tr class="odd">
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="even">
<td>South</td>
<td>South Atlantic</td>
<td>DE</td>
<td>X</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td>0.9</td>
<td>0.8</td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td>DC</td>
<td>X</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td> </td>
<td></td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td>FL</td>
<td>X</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td> </td>
<td></td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td>GA</td>
<td>X</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td> </td>
<td></td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td>MD</td>
<td>X</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td> </td>
<td></td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td>NC</td>
<td>X</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td> </td>
<td></td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td>SC</td>
<td>X</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td> </td>
<td></td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td>VA</td>
<td>X</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td> </td>
<td></td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td>WV</td>
<td>X</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td> </td>
<td></td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td> </td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td> </td>
<td></td>
</tr>
<tr class="even">
<td></td>
<td>East South Central</td>
<td>AL</td>
<td></td>
<td></td>
<td>X</td>
<td></td>
<td></td>
<td>Q</td>
<td>Q</td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td>KY</td>
<td></td>
<td>X</td>
<td></td>
<td></td>
<td></td>
<td> </td>
<td></td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td>MS</td>
<td></td>
<td></td>
<td>X</td>
<td></td>
<td></td>
<td> </td>
<td></td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td>TN</td>
<td></td>
<td>X</td>
<td></td>
<td></td>
<td></td>
<td> </td>
<td></td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td> </td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td> </td>
<td></td>
</tr>
<tr class="odd">
<td></td>
<td>West South Central</td>
<td>AK</td>
<td></td>
<td></td>
<td>X</td>
<td></td>
<td></td>
<td>Q</td>
<td>Q.</td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td>LA</td>
<td></td>
<td></td>
<td>X</td>
<td></td>
<td></td>
<td> </td>
<td></td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td>OK</td>
<td></td>
<td>X</td>
<td></td>
<td></td>
<td></td>
<td> </td>
<td></td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td>TX</td>
<td></td>
<td></td>
<td>X</td>
<td></td>
<td></td>
<td> </td>
<td></td>
</tr>
<tr class="odd">
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="even">
<td>Midwest</td>
<td>East North Central</td>
<td>IL</td>
<td></td>
<td>X</td>
<td></td>
<td></td>
<td></td>
<td>0.5</td>
<td>0.4</td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td>IN</td>
<td></td>
<td>X</td>
<td></td>
<td></td>
<td></td>
<td> </td>
<td></td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td>MI</td>
<td></td>
<td>X</td>
<td></td>
<td></td>
<td></td>
<td> </td>
<td></td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td>OH</td>
<td></td>
<td>X</td>
<td></td>
<td></td>
<td></td>
<td> </td>
<td></td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td>WI</td>
<td></td>
<td>X</td>
<td></td>
<td></td>
<td></td>
<td> </td>
<td></td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td> </td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td> </td>
<td></td>
</tr>
<tr class="even">
<td></td>
<td>West North Central</td>
<td>IA</td>
<td></td>
<td>X</td>
<td></td>
<td></td>
<td></td>
<td>0.4</td>
<td>0.4</td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td>KS</td>
<td></td>
<td>X</td>
<td></td>
<td></td>
<td></td>
<td> </td>
<td></td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td>MN</td>
<td></td>
<td>X</td>
<td></td>
<td></td>
<td></td>
<td> </td>
<td></td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td>MO</td>
<td></td>
<td>X</td>
<td></td>
<td></td>
<td></td>
<td> </td>
<td></td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td>NE</td>
<td></td>
<td>X</td>
<td></td>
<td></td>
<td></td>
<td> </td>
<td></td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td>ND</td>
<td></td>
<td>X</td>
<td></td>
<td></td>
<td></td>
<td> </td>
<td></td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td>SD</td>
<td></td>
<td>X</td>
<td></td>
<td></td>
<td></td>
<td> </td>
<td></td>
</tr>
<tr class="odd">
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="even">
<td>West</td>
<td>Mountain</td>
<td>AZ</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td>X</td>
<td> Q.</td>
<td>Q.</td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td>CO</td>
<td></td>
<td></td>
<td></td>
<td>X</td>
<td></td>
<td> </td>
<td></td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td>ID</td>
<td></td>
<td></td>
<td></td>
<td>X</td>
<td></td>
<td> </td>
<td></td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td>MT</td>
<td></td>
<td></td>
<td></td>
<td>X</td>
<td></td>
<td> </td>
<td></td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td>NV</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td>X</td>
<td> </td>
<td></td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td>NM</td>
<td></td>
<td></td>
<td>X</td>
<td></td>
<td></td>
<td> </td>
<td></td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td>UT</td>
<td></td>
<td></td>
<td></td>
<td>X</td>
<td></td>
<td> </td>
<td></td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td>WY</td>
<td></td>
<td></td>
<td></td>
<td>X</td>
<td></td>
<td> </td>
<td></td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td> </td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td> </td>
<td></td>
</tr>
<tr class="odd">
<td></td>
<td>Pacific</td>
<td>AK</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td>X</td>
<td> 0.2</td>
<td>Q.</td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td>CA</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td>X</td>
<td> </td>
<td></td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td>HI</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td>X</td>
<td> </td>
<td></td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td>OR</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td>X</td>
<td> </td>
<td></td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td>WA</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td>X</td>
<td> </td>
<td></td>
</tr>
<tr class="even">
<td colspan="10"><p>Notes:</p>
<p>Q = Data withheld either because the Relative Standard Error (RSE)
was greater than 50 percent or fewer than 10 households were
sampled.</p>
<p>Source households with heating oil space heating: Energy Information
Administration, Residential Energy Consumption Survey, 2001; Consumption
and Expenditure Data Tables; Tables CE2-9c, CE2-10c, CE2-11c, and
CE2-12c; accessed January 10, 2005
(http://www.eia.doe.gov/emeu/recs/).</p></td>
</tr>
</tbody>
</table>

  -------------------------------------- --------------- ----------- ------------- ---------- ----------
  []{#AppendixA3 .anchor}**Appendix A3.                                                       
  Residential Sector Use of Heating Oil,                                                      
  2001**                                                                                      

                                                                                              

                                         **Census                                             
                                         Region**                                             

                                         **Northeast**   **South**   **Midwest**   **West**   **U.S.**

                                                                                              

  **Number of homes that used Heating                                                         
  Oil (millions)**                                                                            

  Any use (1)                            6.613           0.942       0.885         0.220      8.667

  Space heating (1)                      6.416           0.943       0.886         0.218      8.462

  Primary fuel                           6.240           0.820       0.790         0.170      8.020

  Secondary fuel (2)                     0.176           0.123       0.076         0.048      0.442

  Water Heating (1)                      4.455           Q           Q             Q          4.703

                                                                                              

  **Total volume of Heating Oil used                                                          
  (million gallons)**                                                                         

  Any use                                4,166           419         437           84         5,105

  Space heating                          3,266           379         434           77         4,155

  Primary fuel                           3,226           369         405           64         4,064

  Secondary fuel                         40              10          29            13         91

  Water Heating                          900             40          3             7          950

                                                                                              

  **Heating Oil consumption per                                                               
  household (gallons)**                                                                       

  Any use                                630             445         494           381        589

  Space heating                          509             402         490           353        491

  Primary fuel                           517             450         513           378        507

  Secondary fuel                         227             81          382           271        206

  Water Heating                          202             Q           Q             Q          202

  Notes:                                                                                      

  Q = Data withheld either because the                                                        
  Relative Standard Error (RSE) was                                                           
  greater than 50 percent or fewer than                                                       
  10 households were sampled.                                                                 

  Regions may not add to U.S. total                                                           
  because of rounding errors.                                                                 

  \(1\) Calculated from total volumes                                                         
  and volumes per household reported by                                                       
  the EIA Residential Energy Consumption                                                      
  Survey                                                                                      

  \(2\) Calculated by difference between                                                      
  total and primary space heating.                                                            

  Sources:                                                                                    

  Energy Information Administration,                                                          
  Residential Energy Consumption Survey,                                                      
  2001; Consumption and Expenditure Data                                                      
  Tables; Tables 2, CE2-9c, CE2-10c,                                                          
  CE2-11c, and CE2-12c; accessed January                                                      
  10, 2005                                                                                    
  (http://www.eia.doe.gov/emeu/recs/).                                                        
  -------------------------------------- --------------- ----------- ------------- ---------- ----------

  -------------------------------------- --------------- ----------- ------------- ---------- -----------
  []{#AppendixA4 .anchor}**Appendix A4.                                                       
  Residential Sector Use of Heating Oil,                                                      
  1997**                                                                                      

                                                                                              

                                         **Census                                             
                                         Region**                                             

                                         **Northeast**   **South**   **Midwest**   **West**   **U.S.**

                                                                                              

  **Number of homes that used heating                                                         
  oil (millions)**                                                                            

  Any use (1)                            7.466           1.161       1.052         0.280      9.963

  Space heating (1)                      7.343           1.138       1.052         0.280      9.813

  Primary fuel                           7.080           1.110       1.030         0.230      9.450

  Secondary fuel (2)                     0.263           0.028       0.022         0.050      0.363

  Water Heating (1)                      4.946           Q           Q             Q          1.560

                                                                                              

  **Total volume of heating oil used                                                          
  (million gallons)**                                                                         

  Any use                                5,816           555         752           151        7,273

  Space heating                          4,722           526         734           151        6,133

  Primary fuel                           4,630           520         719           137        6,006

  Secondary fuel                         92              6           15            14         127

  Water Heating                          1,093           29          18            0          1,139

                                                                                              

  **Heating oil consumption per                                                               
  household (gallons)**                                                                       

  Any use                                779             478         715           540        730

  Space heating                          643             462         698           540        625

  Primary fuel                           654             469         698           595        636

  Secondary fuel                         350             214         682           280        350

  Water Heating                          221             Q           Q             Q          219

  Notes:                                                                                      

  Q = Data withheld either because the                                                        
  Relative Standard Error (RSE) was                                                           
  greater than 50 percent or fewer than                                                       
  10 households were sampled.                                                                 

  Regions may not add to U.S. total                                                           
  because of rounding errors.                                                                 

  \(1\) Calculated from total volumes                                                         
  and volumes per household reported by                                                       
  the EIA Residential Energy Consumption                                                      
  Survey                                                                                      

  \(2\) Calculated by difference between                                                      
  total and primary space heating.                                                            

  Sources:                                                                                    

  Energy Information Administration,                                                          
  Residential Energy Consumption Survey,                                                      
  2001; Consumption and Expenditure Data                                                      
  Tables; Tables 2, CE2-9c, CE2-10c,                                                          
  CE2-11c, and CE2-12c; accessed January                                                      
  10, 2005                                                                                    
  (http://www.eia.doe.gov/emeu/recs/).                                                        
  -------------------------------------- --------------- ----------- ------------- ---------- -----------

# Appendix B. Variable Definitions

  --------------- ----------- ----------------------------------------- ------------- --------------
  **Table B1.                                                                         
  Variable                                                                            
  Definitions**                                                                       

                                                                        **Sources**   

  **Variable      **Units**   **Definition**                            **History**   **Forecast**
  Name**                                                                              

  APR             Integer     = 1 if April, 0 otherwise                               

  AUG             Integer     = 1 if August, 0 otherwise                              

  CICPIUS         Index       Consumer price index                      BLS           DRI

  D00             Integer     = 1 if 2000, 0 otherwise                                

  D0001           Integer     = 1 if January 2000, 0 otherwise                        

  D0002           Integer     = 1 if February 2000, 0 otherwise                       

  D0003           Integer     = 1 if March 2000, 0 otherwise                          

  D0009           Integer     = 1 if September 2000, 0 otherwise                      

  D01             Integer     = 1 if 2001, 0 otherwise                                

  D0101           Integer     = 1 if January 2001, 0 otherwise                        

  D0102           Integer     = 1 if February 2001, 0 otherwise                       

  D0103           Integer     = 1 if March 2001, 0 otherwise                          

  D0112           Integer     = 1 if December 2001, 0 otherwise                       

  D0201           Integer     = 1 if January 2002, 0 otherwise                        

  D0202           Integer     = 1 if February 2002, 0 otherwise                       

  D0212           Integer     = 1 if December 2002, 0 otherwise                       

  D03ON           Integer     = 1 if January 2003 or later, 0 otherwise               

  D0403           Integer     = 1 if March 2004, 0 otherwise                          

  D2RCAMW         CPG         Heating oil residential price after       STF           STF
                              taxes, Midwest census region, all sellers               

  D2RCANE         CPG         Heating oil residential price after       STF           STF
                              taxes, Northeast census region, all                     
                              sellers                                                 

  D2RCASO         CPG         Heating oil residential price after       STF           STF
                              taxes, South census region, all sellers                 

  D2RCAUS         CPG         Heating oil residential price after       STF           STF
                              taxes, U.S., all sellers                                

  D2RCAWE         CPG         Heating oil residential price after       STF           STF
                              taxes, West census region, all sellers                  

  D2RCPMW         MMBD        Heating oil residential deliveries,       PMM           STF
                              Midwest census region, all sellers                      

  D2RCPNE         MMBD        Heating oil residential deliveries,       PMM           STF
                              Northeast census region, all sellers                    

  D2RCPSO         MMBD        Heating oil residential deliveries, South PMM           STF
                              census region, all sellers                              

  D2RCPUS         MMBD        Heating oil residential deliveries, U.S., PMM           STF
                              all sellers                                             

  D2RCPWE         MMBD        Heating oil residential deliveries, West  PMM           STF
                              census region, all sellers                              

  D2RCUMW         CPG         Heating oil residential price before      PMM           STF
                              taxes, Midwest census region, all sellers               

  D2RCUNE         CPG         Heating oil residential price before      PMM           STF
                              taxes, Northeast census region, all                     
                              sellers                                                 

  D2RCUSO         CPG         Heating oil residential price before      PMM           STF
                              taxes, South census region, all sellers                 

  D2RCUUS         CPG         Heating oil residential price before      PMM           STF
                              taxes, U.S., all sellers                                

  D2RCUWE         CPG         Heating oil residential price before      PMM           STF
                              taxes, West census region, all sellers                  

  D2TXUMW         Index       Heating oil State sales tax factor,       STF           ROT
                              Midwest census region                                   

  D2TXUNE         Index       Heating oil State sales tax factor,       STF           ROT
                              Northeast census region                                 

  D2TXUSO         Index       Heating oil State sales tax factor, South STF           ROT
                              census region                                           

  D2TXUWE         Index       Heating oil State sales tax factor, West  STF           ROT
                              census region                                           

  D9602           Integer     = 1 if February 1996, 0 otherwise                       

  D98             Integer     = 1 if 1998, 0 otherwise                                

  D99             Integer     = 1 if 1999, 0 otherwise                                

  D9912           Integer     = 1 if December 1999, 0 otherwise                       

  DEC             Integer     = 1 if December, 0 otherwise                            

  DFPSPP1BLD      MMB         Distillate stock change, PADD 1           PSM           STF

  DFPSPP2BLD      MMB         Distillate stock change, PADD 2           PSM           STF

  DFPSPP3BLD      MMB         Distillate stock change, PADD 3           PSM           STF

  DFPSPP4BLD      MMB         Distillate stock change, PADD 4           PSM           STF

  DFPSPP5BLD      MMB         Distillate stock change, PADD 5           PSM           STF

  DFPSPP1         MMB         Distillate end-of-month stocks, PADD 1    PSM           STF

  DFPSPP2         MMB         Distillate end-of-month stocks, PADD 2    PSM           STF

  DFPSPP3         MMB         Distillate end-of-month stocks, PADD 3    PSM           STF

  DFPSPP4         MMB         Distillate end-of-month stocks, PADD 4    PSM           STF

  DFPSPP5         MMB         Distillate end-of-month stocks, PADD 5    PSM           STF

  DFPSPUS         MMB         Distillate end-of-month stocks, U.S.      PSM           STF

  FEB             Integer     = 1 if February, 0 otherwise                            

  JAN             Integer     = 1 if January, 0 otherwise                             

  JUL             Integer     = 1 if July, 0 otherwise                                

  JUN             Integer     = 1 if June, 0 otherwise                                

  MAR             Integer     = 1 if March, 0 otherwise                               

  MAY             Integer     = 1 if May, 0 otherwise                                 

  NOV             Integer     = 1 if November, 0 otherwise                            

  OCT             Integer     = 1 if October, 0 otherwise                             

  QHFORECS_ESC    MM          Number of homes with heating oil as       RECS          ROT
                              primary space heating fuel, E. South                    
                              Central census div.                                     

  QHFORECS_ENC    MM          Number of homes with heating oil as       RECS          ROT
                              primary space heating fuel, E. North                    
                              Central census div.                                     

  QHFORECS_MAC    MM          Number of homes with heating oil as       RECS          ROT
                              primary space heating fuel, Middle                      
                              Atlantic census div.                                    

  QHFORECS_MTN    MM          Number of homes with heating oil as       RECS          ROT
                              primary space heating fuel, Mountain                    
                              census div.                                             

  QHFORECS_NEC    MM          Number of homes with heating oil as       RECS          ROT
                              primary space heating fuel, New England                 
                              census div.                                             

  QHFORECS_PAC    MM          Number of homes with heating oil as       RECS          ROT
                              primary space heating fuel, Pacific                     
                              census div.                                             

  QHFORECS_SAC    MM          Number of homes with heating oil as       RECS          ROT
                              primary space heating fuel, South                       
                              Atlantic census div.                                    

  QHFORECS_WNC    MM          Number of homes with heating oil as       RECS          ROT
                              primary space heating fuel, W. North                    
                              Central census div.                                     

  QHFORECS_WSC    MM          Number of homes with heating oil as       RECS          ROT
                              primary space heating fuel, W. South                    
                              Central census div.                                     

  SEP             Integer     = 1 if September, 0 otherwise                           

  TIME            Integer     = 1 to n, where n = number of observation               

  WTIPUUS         DBBL        Spot price WTI crude oil                  PMM           EXO

  ZWHDENC         HDD         Heating degree-days, East North Central   NOAA          NOAA
                              census division                                         

  ZWHDESC         HDD         Heating degree-days, East South Central   NOAA          NOAA
                              census division                                         

  ZWHDMTN         HDD         Heating degree-days, Mountain census      NOAA          NOAA
                              division                                                

  ZWHDPAC         HDD         Heating degree-days, Pacific census       NOAA          NOAA
                              division                                                

  ZWHDPMA         HDD         Heating degree-days, Middle Atlantic      NOAA          NOAA
                              census division                                         

  ZWHDPNE         HDD         Heating degree-days, New England census   NOAA          NOAA
                              division                                                

  ZWHDSAC         HDD         Heating degree-days, South Atlantic       NOAA          NOAA
                              census division                                         

  ZWHDWNC         HDD         Heating degree-days, West. North Central  NOAA          NOAA
                              census division                                         

  ZWHDWSC         HDD         Heating degree-days, West South Central   NOAA          NOAA
                              census division                                         

  ZWHNENC         HDD         Heating degree-days normal, East North    NOAA          NOAA
                              Central census division                                 

  ZWHNESC         HDD         Heating degree-days normal, East South    NOAA          NOAA
                              Central census division                                 

  ZWHNMTN         HDD         Heating degree-days normal, Mountain      NOAA          NOAA
                              census division                                         

  ZWHNPAC         HDD         Heating degree-days normal, Pacific       NOAA          NOAA
                              census division                                         

  ZWHNPMA         HDD         Heating degree-days normal, Middle        NOAA          NOAA
                              Atlantic census division                                

  ZWHNPNE         HDD         Heating degree-days normal, New England   NOAA          NOAA
                              census division                                         

  ZWHNSAC         HDD         Heating degree-days normal, South         NOAA          NOAA
                              Atlantic census division                                

  ZWHNWNC         HDD         Heating degree-days normal, West. North   NOAA          NOAA
                              Central census division                                 

  ZWHNWSC         HDD         Heating degree-days normal, West South    NOAA          NOAA
                              Central census division                                 
  --------------- ----------- ----------------------------------------- ------------- --------------

  --------- ------------------ ------- --------- -----------------------------
  **Table                              **Table   
  B2. Units                            B3.       
  Key**                                Sources   
                                       Key**     

  CPG       Cents per gallon           BLS       Bureau of Labor Statistics

  DBBL      Dollars per barrel         DRI       Global Insight DRI-WEFA

  DMMB      Dollars per                EXO       Exogenous
            million Btu                          

  HDD       Heating                    NGM       Natural Gas Monthly
            degree-days                          

  Index     Index value                NOAA      National Oceanic and
                                                 Atmospheric Organization

  MM        Millions                   PMM       Petroleum Marketing Monthly

  MMB       Million barrels            PSM       Petroleum Supply Monthly

  MMBD      Million barrels            RECS      Residential Energy
            per day                              Consumption Survey

                                       ROT       Rule-of-thumb

                                       STF       STIFS Model
  --------- ------------------ ------- --------- -----------------------------

[]{#__RefHeading___Toc101145815 .anchor}**Appendix C. EViews Model
Program File**

\'\-\-\-\-\-\-\-\-- Residential heating oil consumption
\-\-\-\-\-\-\-\--

:EQ_D2RCPNE

:EQ_D2RCPSO

:EQ_D2RCPMW

:EQ_D2RCPWE

\@IDENTITY d2rcpus = d2rcpne + d2rcpso + d2rcpmw + d2rcpwe

\' \-\-\-\-\-\-\-- Residential Heating Oil Prices \-\-\-\-\-\-\-\--

:EQ_D2RCUNE

:EQ_D2RCUSO

:EQ_D2RCUMW

:EQ_D2RCUWE

\@IDENTITY d2rcuus = (d2rcpne \* d2rcune + d2rcpso \* d2rcuso + d2rcpmw
\* d2rcumw + d2rcpwe \* d2rcuwe) / d2rcpus

\' \-\-\-\-\-\-\-\-\-- Add state sales taxes to PMM price series
\-\-\-\-\-\-\-\-\--

\@IDENTITY d2rcane = d2rcune \* d2txune

\@IDENTITY d2rcaso = d2rcuso \* d2txuso

\@IDENTITY d2rcamw = d2rcumw \* d2txumw

\@IDENTITY d2rcawe = d2rcuwe \* d2txuwe

\@IDENTITY d2rcaus = (d2rcpne \* d2rcane + d2rcpso \* d2rcaso + d2rcpmw
\* d2rcamw + d2rcpwe \* d2rcawe) / d2rcpus

\' \-\-\-\-\-\-\-- Distillate Stocks \-\-\-\-\-\-\-\--

:EQ_DFPSP1BLD

:EQ_DFPSP2BLD

:EQ_DFPSP3BLD

:EQ_DFPSP4BLD

:EQ_DFPSP5BLD

\@IDENTITY dfpspp1 = dfpspp1(-1) + dfpsp1bld

\@IDENTITY dfpspp2 = dfpspp2(-1) + dfpsp2bld

\@IDENTITY dfpspp3 = dfpspp3(-1) + dfpsp3bld

\@IDENTITY dfpspp4 = dfpspp4(-1) + dfpsp4bld

\@IDENTITY dfpspp5 = dfpspp5(-1) + dfpsp5bld

\@IDENTITY dfpspus = dfpspp1 + dfpspp2 + dfpspp3 + dfpspp4 + dfpspp5

[]{#__RefHeading___Toc101145816 .anchor}**Appendix D. Regression
Results**

D2RCPNE, Residential Heating Oil Demand Northeast Census Region

D2RCPSO, Residential Heating Oil Demand South Census Region

D2RCPMW, Residential Heating Oil Demand Midwest Census Region

D2RCPWE, Residential Heating Oil Demand West Census Region

D2WHUUS, Distillate Wholesale Price

D2RCUNE, Heating Oil Retail Residential Price excluding taxes, Northeast
Census Region

D2RCUSO, Heating Oil Retail Residential Price excluding taxes, South
Census Region

D2RCUMW, Heating Oil Retail Residential Price excluding taxes, Midwest
Census Region

D2RCUWE, Heating Oil Retail Residential Price excluding taxes, West
Census Region

DFPSP1BLD, Distillate Stock Build (Draw) PADD 1

DFPSP2BLD, Distillate Stock Build (Draw) PADD 2

DFPSP3BLD, Distillate Stock Build (Draw) PADD 3

DFPSP4BLD, Distillate Stock Build (Draw) PADD 4

DFPSP5BLD, Distillate Stock Build (Draw) PADD 5

  --------------------------------------------------------------------------------------------------------------------------------------------------------------- ------------- ----------------------- ------------- -----------
  Dependent Variable: D2RCPNE                                                                                                                                                                                         

  Method: Least Squares                                                                                                                                                                                               

  Date: 02/08/05 Time: 13:27                                                                                                                                                                                          

  Sample: 1995M01 2004M10                                                                                                                                                                                             

  Included observations: 118                                                                                                                                                                                          

                                                                                                                                                                                                                      

                                                                                                                                                                                                                      

  Variable                                                                                                                                                        Coefficient   Std. Error              t-Statistic   Prob.  

                                                                                                                                                                                                                      

                                                                                                                                                                                                                      

  C                                                                                                                                                               0.673806      0.262166                2.570149      0.0117

  (D2RCUNE/CICPIUS)\*(NOV+DEC+JAN+FEB+MAR)                                                                                                                        -0.002172     0.000389                -5.585599     0.0000

  (D2RCUNE/CICPIUS)\*(APR+MAY+JUN+JUL+AUG+SEP+OCT)                                                                                                                3.85E-05      0.000312                0.123402      0.9020

  ((QHFOHRECS_MAC\*(ZWHDPMA-ZWHNPMA)/ZSAJQUS)+(QHFOHRECS_NEC\*(ZWHDPNE-ZWHNPNE)/ZSAJQUS))/(QHFOHRECS_MAC+QHFOHRECS_NEC)                                           0.011937      0.000779                15.32149      0.0000

  ((QHFOHRECS_MAC(-1)\*(ZWHDPMA(-1)-ZWHNPMA(-1))/ZSAJQUS(-1))+(QHFOHRECS_NEC(-1)\*(ZWHDPNE(-1)-ZWHNPNE(-1))/ZSAJQUS(-1)))/(QHFOHRECS_MAC(-1)+QHFOHRECS_NEC(-1))   0.002752      0.000808                3.406135      0.0010

  (QHFOHRECS_MAC+QHFOHRECS_NEC)\*(NOV+DEC+JAN+FEB+MAR)                                                                                                            0.018699      0.026349                0.709654      0.4796

  (QHFOHRECS_MAC+QHFOHRECS_NEC)\*(APR+MAY+JUN+JUL+AUG+SEP+OCT)                                                                                                    0.000946      0.008915                0.106159      0.9157

  TIME\*(NOV+DEC+JAN+FEB+MAR)                                                                                                                                     -0.000690     0.000298                -2.317811     0.0226

  D99+D00+D01                                                                                                                                                     -0.020101     0.004196                -4.789838     0.0000

  D0202                                                                                                                                                           -0.074884     0.021289                -3.517561     0.0007

  (D0212+D03ON)\*(NOV+DEC+JAN+FEB+MAR)                                                                                                                            0.077695      0.011872                6.544312      0.0000

  JAN                                                                                                                                                             0.099329      0.008553                11.61358      0.0000

  FEB                                                                                                                                                             0.084143      0.008922                9.430877      0.0000

  MAR                                                                                                                                                             -0.056617     0.008587                -6.593536     0.0000

  APR                                                                                                                                                             -0.432486     0.273475                -1.581449     0.1171

  MAY                                                                                                                                                             -0.544798     0.273382                -1.992811     0.0491

  JUN                                                                                                                                                             -0.591072     0.273239                -2.163204     0.0330

  JUL                                                                                                                                                             -0.603631     0.273063                -2.210596     0.0294

  AUG                                                                                                                                                             -0.584555     0.273080                -2.140597     0.0348

  SEP                                                                                                                                                             -0.538940     0.273347                -1.971634     0.0515

  OCT                                                                                                                                                             -0.484139     0.273498                -1.770176     0.0799

  NOV                                                                                                                                                             -0.161173     0.008762                -18.39377     0.0000

                                                                                                                                                                                                                      

                                                                                                                                                                                                                      

  R-squared                                                                                                                                                       0.991113          Mean dependent var                0.258277

  Adjusted R-squared                                                                                                                                              0.989169          S.D. dependent var                0.175750

  S.E. of regression                                                                                                                                              0.018291          Akaike info                       -4.998274
                                                                                                                                                                                criterion                             

  Sum squared resid                                                                                                                                               0.032118          Schwarz criterion                 -4.481705

  Log likelihood                                                                                                                                                  316.8981          F-statistic                       509.8090

  Durbin-Watson stat                                                                                                                                              2.239524          Prob(F-statistic)                 0.000000

                                                                                                                                                                                                                      

                                                                                                                                                                                                                      
  --------------------------------------------------------------------------------------------------------------------------------------------------------------- ------------- ----------------------- ------------- -----------

  ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- ------------- ----------------------- ------------- -----------
  Dependent Variable: D2RCPSO                                                                                                                                                                                                                                                                  

  Method: Least Squares                                                                                                                                                                                                                                                                        

  Date: 02/08/05 Time: 13:44                                                                                                                                                                                                                                                                   

  Sample: 1995M01 2004M10                                                                                                                                                                                                                                                                      

  Included observations: 118                                                                                                                                                                                                                                                                   

                                                                                                                                                                                                                                                                                               

                                                                                                                                                                                                                                                                                               

  Variable                                                                                                                                                                                                                                 Coefficient   Std. Error              t-Statistic   Prob.  

                                                                                                                                                                                                                                                                                               

                                                                                                                                                                                                                                                                                               

  C                                                                                                                                                                                                                                        0.108626      0.010405                10.43966      0.0000

  (D2RCUSO/CICPIUS)\*(NOV+DEC+JAN+FEB+MAR)                                                                                                                                                                                                 -0.000302     0.000101                -2.999208     0.0034

  (D2RCUSO/CICPIUS)\*(APR+MAY+JUN+JUL+AUG+SEP+OCT)                                                                                                                                                                                         -5.39E-05     7.94E-05                -0.678948     0.4988

  ((QHFOHRECS_ESC\*(ZWHDESC-ZWHNESC)/ZSAJQUS+QHFOHRECS_WSC\*(ZWHDWSC-ZWHNWSC)/ZSAJQUS+QHFOHRECS_SAC\*(ZWHDPSA-ZWHNPSA)/ZSAJQUS))/(QHFOHRECS_ESC+QHFOHRECS_WSC+QHFOHRECS_SAC)                                                               0.002536      0.000285                8.905017      0.0000

  ((QHFOHRECS_ESC(-1)\*(ZWHDESC(-1)-ZWHNESC(-1))/ZSAJQUS(-1)+QHFOHRECS_WSC(-1)\*(ZWHDWSC(-1)-ZWHNWSC(-1))/ZSAJQUS(-1)+QHFOHRECS_SAC(-1)\*(ZWHDPSA(-1)-ZWHNPSA(-1))/ZSAJQUS(-1)))/(QHFOHRECS_ESC(-1)+QHFOHRECS_WSC(-1)+QHFOHRECS_SAC(-1))   0.000558      0.000307                1.819683      0.0719

  (QHFOHRECS_ESC+QHFOHRECS_WSC+QHFOHRECS_SAC)\*(NOV+DEC+JAN+FEB+MAR)                                                                                                                                                                       -0.015625     0.004989                -3.132117     0.0023

  (QHFOHRECS_ESC+QHFOHRECS_WSC+QHFOHRECS_SAC)\*(APR+MAY+JUN+JUL+AUG+SEP+OCT)                                                                                                                                                               0.000433      0.003962                0.109330      0.9132

  D0101+D0102+D0103                                                                                                                                                                                                                        -0.018030     0.003066                -5.879569     0.0000

  D0112+D0201+D0202                                                                                                                                                                                                                        -0.029830     0.003331                -8.954669     0.0000

  JAN                                                                                                                                                                                                                                      0.021426      0.002250                9.523603      0.0000

  FEB                                                                                                                                                                                                                                      0.013843      0.002307                5.999421      0.0000

  MAR                                                                                                                                                                                                                                      -0.014409     0.002292                -6.286787     0.0000

  APR                                                                                                                                                                                                                                      -0.076558     0.013148                -5.822653     0.0000

  MAY                                                                                                                                                                                                                                      -0.089531     0.012998                -6.888277     0.0000

  JUN                                                                                                                                                                                                                                      -0.091690     0.012907                -7.103700     0.0000

  JUL                                                                                                                                                                                                                                      -0.092406     0.012884                -7.171967     0.0000

  AUG                                                                                                                                                                                                                                      -0.088848     0.012911                -6.881758     0.0000

  SEP                                                                                                                                                                                                                                      -0.081657     0.013009                -6.276702     0.0000

  OCT                                                                                                                                                                                                                                      -0.071190     0.013102                -5.433628     0.0000

  NOV                                                                                                                                                                                                                                      -0.028744     0.002286                -12.57103     0.0000

                                                                                                                                                                                                                                                                                               

                                                                                                                                                                                                                                                                                               

  R-squared                                                                                                                                                                                                                                0.974768          Mean dependent var                0.040121

  Adjusted R-squared                                                                                                                                                                                                                       0.969876          S.D. dependent var                0.027375

  S.E. of regression                                                                                                                                                                                                                       0.004751          Akaike info                       -7.707506
                                                                                                                                                                                                                                                         criterion                             

  Sum squared resid                                                                                                                                                                                                                        0.002212          Schwarz criterion                 -7.237898

  Log likelihood                                                                                                                                                                                                                           474.7428          F-statistic                       199.2580

  Durbin-Watson stat                                                                                                                                                                                                                       1.754904          Prob(F-statistic)                 0.000000

                                                                                                                                                                                                                                                                                               

                                                                                                                                                                                                                                                                                               
  ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- ------------- ----------------------- ------------- -----------

  --------------------------------------------------------------------------------------------------------------------------------------------------------------- ------------- ----------------------- ------------- -----------
  Dependent Variable: D2RCPMW                                                                                                                                                                                         

  Method: Least Squares                                                                                                                                                                                               

  Date: 02/08/05 Time: 13:36                                                                                                                                                                                          

  Sample: 1995M01 2004M10                                                                                                                                                                                             

  Included observations: 118                                                                                                                                                                                          

                                                                                                                                                                                                                      

                                                                                                                                                                                                                      

  Variable                                                                                                                                                        Coefficient   Std. Error              t-Statistic   Prob.  

                                                                                                                                                                                                                      

                                                                                                                                                                                                                      

  C                                                                                                                                                               0.049376      0.009020                5.474281      0.0000

  (D2RCUMW/CICPIUS)\*(NOV+DEC+JAN+FEB+MAR)                                                                                                                        -0.000443     8.88E-05                -4.994500     0.0000

  (D2RCUMW/CICPIUS)\*(APR+MAY+JUN+JUL+AUG+SEP+OCT)                                                                                                                -9.48E-05     5.59E-05                -1.694802     0.0933

  ((QHFOHRECS_ENC\*(ZWHDENC-ZWHNENC)/ZSAJQUS)+(QHFOHRECS_WNC\*(ZWHDWNC-ZWHNWNC)/ZSAJQUS))/(QHFOHRECS_ENC+QHFOHRECS_WNC)                                           0.001414      0.000134                10.51765      0.0000

  ((QHFOHRECS_ENC(-1)\*(ZWHDENC(-1)-ZWHNENC(-1))/ZSAJQUS(-1))+(QHFOHRECS_WNC(-1)\*(ZWHDWNC(-1)-ZWHNWNC(-1))/ZSAJQUS(-1)))/(QHFOHRECS_ENC(-1)+QHFOHRECS_WNC(-1))   0.000307      0.000136                2.259744      0.0261

  (QHFOHRECS_ENC+QHFOHRECS_WNC)\*(NOV+DEC+JAN+FEB+MAR)                                                                                                            0.037238      0.005528                6.736209      0.0000

  (QHFOHRECS_ENC+QHFOHRECS_WNC)\*(APR+MAY+JUN+JUL+AUG+SEP+OCT)                                                                                                    -0.016043     0.003756                -4.271162     0.0000

  D99+D00+D01                                                                                                                                                     -0.007579     0.000869                -8.725147     0.0000

  D0202                                                                                                                                                           -0.017877     0.004225                -4.231725     0.0001

  (D0212+D03ON)\*(NOV+DEC+JAN+FEB+MAR)                                                                                                                            0.012108      0.001955                6.192882      0.0000

  JAN                                                                                                                                                             0.010830      0.001717                6.307180      0.0000

  FEB                                                                                                                                                             0.007419      0.001797                4.127618      0.0001

  MAR                                                                                                                                                             -0.011106     0.001733                -6.407548     0.0000

  APR                                                                                                                                                             0.003153      0.010428                0.302370      0.7630

  MAY                                                                                                                                                             -0.009611     0.010382                -0.925681     0.3569

  JUN                                                                                                                                                             -0.014417     0.010338                -1.394508     0.1663

  JUL                                                                                                                                                             -0.014649     0.010307                -1.421208     0.1585

  AUG                                                                                                                                                             -0.010353     0.010331                -1.002202     0.3187

  SEP                                                                                                                                                             -0.000354     0.010386                -0.034109     0.9729

  OCT                                                                                                                                                             0.007336      0.010415                0.704376      0.4829

  NOV                                                                                                                                                             -0.015074     0.001762                -8.553469     0.0000

                                                                                                                                                                                                                      

                                                                                                                                                                                                                      

  R-squared                                                                                                                                                       0.973651          Mean dependent var                0.034405

  Adjusted R-squared                                                                                                                                              0.968219          S.D. dependent var                0.020814

  S.E. of regression                                                                                                                                              0.003711          Akaike info                       -8.195326
                                                                                                                                                                                criterion                             

  Sum squared resid                                                                                                                                               0.001335          Schwarz criterion                 -7.702238

  Log likelihood                                                                                                                                                  504.5243          F-statistic                       179.2212

  Durbin-Watson stat                                                                                                                                              1.849846          Prob(F-statistic)                 0.000000

                                                                                                                                                                                                                      

                                                                                                                                                                                                                      
  --------------------------------------------------------------------------------------------------------------------------------------------------------------- ------------- ----------------------- ------------- -----------

  --------------------------------------------------------------------------------------------------------------------------------------------------------------- ------------- ----------------------- ------------- -----------
  Dependent Variable: D2RCPWE                                                                                                                                                                                         

  Method: Least Squares                                                                                                                                                                                               

  Date: 02/08/05 Time: 13:52                                                                                                                                                                                          

  Sample: 1995M01 2004M10                                                                                                                                                                                             

  Included observations: 118                                                                                                                                                                                          

                                                                                                                                                                                                                      

                                                                                                                                                                                                                      

  Variable                                                                                                                                                        Coefficient   Std. Error              t-Statistic   Prob.  

                                                                                                                                                                                                                      

                                                                                                                                                                                                                      

  C                                                                                                                                                               0.010867      0.001191                9.127418      0.0000

  (D2RCUWE/CICPIUS)\*(NOV+DEC+JAN+FEB+MAR)                                                                                                                        1.92E-05      1.36E-05                1.416925      0.1597

  (D2RCUWE/CICPIUS)\*(APR+MAY+JUN+JUL+AUG+SEP+OCT)                                                                                                                -3.58E-05     1.03E-05                -3.470437     0.0008

  ((QHFOHRECS_MTN\*(ZWHDMTN-ZWHNMTN)/ZSAJQUS)+(QHFOHRECS_PAC\*(ZWHDPAC-ZWHNPAC)/ZSAJQUS))/(QHFOHRECS_MTN+QHFOHRECS_PAC)                                           0.000182      4.63E-05                3.930532      0.0002

  ((QHFOHRECS_MTN(-1)\*(ZWHDMTN(-1)-ZWHNMTN(-1))/ZSAJQUS(-1))+(QHFOHRECS_PAC(-1)\*(ZWHDPAC(-1)-ZWHNPAC(-1))/ZSAJQUS(-1)))/(QHFOHRECS_MTN(-1)+QHFOHRECS_PAC(-1))   2.33E-05      4.59E-05                0.507941      0.6127

  (QHFOHRECS_MTN+QHFOHRECS_PAC)\*(NOV+DEC+JAN+FEB+MAR)                                                                                                            0.011597      0.002182                5.313798      0.0000

  (QHFOHRECS_MTN+QHFOHRECS_PAC)\*(APR+MAY+JUN+JUL+AUG+SEP+OCT)                                                                                                    0.006089      0.001943                3.133233      0.0023

  D9602                                                                                                                                                           0.003720      0.000816                4.560793      0.0000

  D98                                                                                                                                                             -0.001059     0.000260                -4.074952     0.0001

  D0009                                                                                                                                                           0.003049      0.000802                3.801798      0.0003

  D0101                                                                                                                                                           -0.002569     0.000827                -3.107868     0.0025

  JAN                                                                                                                                                             0.001910      0.000357                5.356693      0.0000

  FEB                                                                                                                                                             -0.000921     0.000359                -2.562156     0.0120

  MAR                                                                                                                                                             -0.002812     0.000351                -8.016880     0.0000

  APR                                                                                                                                                             -0.001380     0.001508                -0.915417     0.3623

  MAY                                                                                                                                                             -0.004316     0.001509                -2.860669     0.0052

  JUN                                                                                                                                                             -0.006501     0.001499                -4.336597     0.0000

  JUL                                                                                                                                                             -0.007410     0.001492                -4.966065     0.0000

  AUG                                                                                                                                                             -0.006644     0.001486                -4.470296     0.0000

  SEP                                                                                                                                                             -0.004641     0.001491                -3.112015     0.0024

  OCT                                                                                                                                                             -0.000399     0.001510                -0.264294     0.7921

  NOV                                                                                                                                                             -0.003182     0.000355                -8.968769     0.0000

                                                                                                                                                                                                                      

                                                                                                                                                                                                                      

  R-squared                                                                                                                                                       0.979685          Mean dependent var                0.008601

  Adjusted R-squared                                                                                                                                              0.975241          S.D. dependent var                0.004766

  S.E. of regression                                                                                                                                              0.000750          Akaike info                       -11.38654
                                                                                                                                                                                criterion                             

  Sum squared resid                                                                                                                                               5.40E-05          Schwarz criterion                 -10.86997

  Log likelihood                                                                                                                                                  693.8058          F-statistic                       220.4541

  Durbin-Watson stat                                                                                                                                              1.491237          Prob(F-statistic)                 0.000000

                                                                                                                                                                                                                      

                                                                                                                                                                                                                      
  --------------------------------------------------------------------------------------------------------------------------------------------------------------- ------------- ----------------------- ------------- -----------

  --------------------------------------------------------------------- ------------- ----------------------- ------------- ----------
  Dependent Variable: D2WHUUS-(100\*WTIPUUS/42)                                                                             

  Method: Least Squares                                                                                                     

  Date: 04/12/05 Time: 13:12                                                                                                

  Sample: 1993M01 2004M12                                                                                                   

  Included observations: 144                                                                                                

  Convergence achieved after 6 iterations                                                                                   

                                                                                                                            

                                                                                                                            

  Variable                                                              Coefficient   Std. Error              t-Statistic   Prob.  

                                                                                                                            

                                                                                                                            

  C                                                                     11.13267      1.133403                9.822337      0.0000

  (100/42)\*(WTIPUUS-WTIPUUS(-1))                                       -0.174984     0.031289                -5.592541     0.0000

  DFTCPUS-(DFTCPUS(-12)+DFTCPUS(-24)+DFTCPUS(-36))/3                    4.423446      1.050675                4.210099      0.0000

  DFPSPUS(-1)-(DFPSPUS(-13)+DFPSPUS(-25)+DFPSPUS(-37)+DFPSPUS(-49))/4   -0.219731     0.037377                -5.878742     0.0000

  D0001+D0002                                                           10.69974      1.675403                6.386365      0.0000

  D0302+D0303                                                           8.679723      1.685867                5.148523      0.0000

  JAN                                                                   0.447500      0.632246                0.707794      0.4804

  FEB                                                                   -0.380240     0.836826                -0.454383     0.6503

  MAR                                                                   -1.686276     0.944460                -1.785439     0.0766

  APR                                                                   -1.442302     1.005085                -1.435004     0.1538

  MAY                                                                   -3.042389     1.049655                -2.898465     0.0044

  JUN                                                                   -4.607060     1.060133                -4.345738     0.0000

  JUL                                                                   -4.178776     1.048637                -3.984960     0.0001

  AUG                                                                   -2.750446     1.007258                -2.730627     0.0072

  SEP                                                                   -0.547539     0.929968                -0.588773     0.5571

  OCT                                                                   0.414402      0.806644                0.513736      0.6083

  NOV                                                                   -0.120740     0.602313                -0.200460     0.8414

  AR(1)                                                                 0.818278      0.059365                13.78385      0.0000

                                                                                                                            

                                                                                                                            

  R-squared                                                             0.855357          Mean dependent var                10.19028

  Adjusted R-squared                                                    0.835841          S.D. dependent var                4.963084

  S.E. of regression                                                    2.010868          Akaike info                       4.351478
                                                                                      criterion                             

  Sum squared resid                                                     509.4923          Schwarz criterion                 4.722705

  Log likelihood                                                        -295.3064         F-statistic                       43.82991

  Durbin-Watson stat                                                    1.878661          Prob(F-statistic)                 0.000000

                                                                                                                            

                                                                                                                            

  Inverted AR Roots                                                           .82                                           

                                                                                                                            

                                                                                                                            
  --------------------------------------------------------------------- ------------- ----------------------- ------------- ----------

  ----------------------------------------------------------------------------------------------------------------------- ------------- ----------------------- ------------- ----------
  Dependent Variable: D2RCUNE-D2WHUUS                                                                                                                                         

  Method: Least Squares                                                                                                                                                       

  Date: 04/12/05 Time: 13:58                                                                                                                                                  

  Sample: 1995M01 2004M12                                                                                                                                                     

  Included observations: 120                                                                                                                                                  

  Convergence achieved after 18 iterations                                                                                                                                    

                                                                                                                                                                              

                                                                                                                                                                              

  Variable                                                                                                                Coefficient   Std. Error              t-Statistic   Prob.  

                                                                                                                                                                              

                                                                                                                                                                              

  C                                                                                                                       41.38390      1.573120                26.30689      0.0000

  D2WHUUS-D2WHUUS(-1)                                                                                                     -0.346061     0.024348                -14.21322     0.0000

  DFPSPP1(-1)-(DFPSPP1(-13)+DFPSPP1(-25)+DFPSPP1(-37)+DFPSPP1(-49))/4                                                     0.044958      0.041261                1.089595      0.2785

  ((QHFOHRECS_MAC\*(ZWHDPMA-ZWHNPMA)/ZSAJQUS)+(QHFOHRECS_NEC\*(ZWHDPNE-ZWHDPNE)/ZSAJQUS))/(QHFOHRECS_MAC+QHFOHRECS_NEC)   -0.050790     0.086307                -0.588476     0.5575

  D0001                                                                                                                   8.157912      1.929766                4.227409      0.0001

  D0002                                                                                                                   15.74686      1.649178                9.548310      0.0000

  D0305+D0306                                                                                                             6.886998      1.241416                5.547696      0.0000

  D00ON                                                                                                                   4.596363      1.880788                2.443849      0.0163

  JAN                                                                                                                     1.634237      0.525465                3.110080      0.0024

  FEB                                                                                                                     2.334782      0.709326                3.291553      0.0014

  MAR                                                                                                                     1.915262      0.792653                2.416268      0.0175

  APR                                                                                                                     -0.181620     0.851126                -0.213388     0.8315

  MAY                                                                                                                     -2.414157     0.896099                -2.694073     0.0083

  JUN                                                                                                                     -3.979434     0.905171                -4.396332     0.0000

  JUL                                                                                                                     -6.677332     0.880078                -7.587205     0.0000

  AUG                                                                                                                     -9.277763     0.846477                -10.96044     0.0000

  SEP                                                                                                                     -8.214147     0.780038                -10.53044     0.0000

  OCT                                                                                                                     -5.867529     0.668303                -8.779751     0.0000

  NOV                                                                                                                     -3.245099     0.494535                -6.561925     0.0000

  AR(1)                                                                                                                   0.874059      0.051378                17.01217      0.0000

                                                                                                                                                                              

                                                                                                                                                                              

  R-squared                                                                                                               0.956658          Mean dependent var                40.55577

  Adjusted R-squared                                                                                                      0.948423          S.D. dependent var                6.773976

  S.E. of regression                                                                                                      1.538411          Akaike info                       3.850390
                                                                                                                                        criterion                             

  Sum squared resid                                                                                                       236.6710          Schwarz criterion                 4.314972

  Log likelihood                                                                                                          -211.0234         F-statistic                       116.1695

  Durbin-Watson stat                                                                                                      1.816469          Prob(F-statistic)                 0.000000

                                                                                                                                                                              

                                                                                                                                                                              

  Inverted AR Roots                                                                                                             .87                                           

                                                                                                                                                                              

                                                                                                                                                                              
  ----------------------------------------------------------------------------------------------------------------------- ------------- ----------------------- ------------- ----------

  ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------- ------------- ----------------------- ------------- ----------
  Dependent Variable: D2RCUSO-D2WHUUS                                                                                                                                                                                              

  Method: Least Squares                                                                                                                                                                                                            

  Date: 04/12/05 Time: 14:01                                                                                                                                                                                                       

  Sample: 1995M01 2004M12                                                                                                                                                                                                          

  Included observations: 120                                                                                                                                                                                                       

  Convergence achieved after 9 iterations                                                                                                                                                                                          

                                                                                                                                                                                                                                   

                                                                                                                                                                                                                                   

  Variable                                                                                                                                                                     Coefficient   Std. Error              t-Statistic   Prob.  

                                                                                                                                                                                                                                   

                                                                                                                                                                                                                                   

  C                                                                                                                                                                            41.09855      1.499891                27.40103      0.0000

  D2WHUUS-D2WHUUS(-1)                                                                                                                                                          -0.345449     0.030892                -11.18264     0.0000

  DFPSPP1(-1)-(DFPSPP1(-13)+DFPSPP1(-25)+DFPSPP1(-37)+DFPSPP1(-49))/4                                                                                                          0.068549      0.050871                1.347522      0.1808

  ((QHFOHRECS_ESC\*(ZWHDESC-ZWHNESC)/ZSAJQUS+QHFOHRECS_WSC\*(ZWHDWSC-ZWHNWSC)/ZSAJQUS+QHFOHRECS_SAC\*(ZWHDPSA-ZWHNPSA)/ZSAJQUS))/(QHFOHRECS_ESC+QHFOHRECS_WSC+QHFOHRECS_SAC)   0.016413      0.100196                0.163806      0.8702

  D0002                                                                                                                                                                        7.190610      1.737181                4.139241      0.0001

  D0305+D0306                                                                                                                                                                  6.276471      1.646747                3.811436      0.0002

  D00ON                                                                                                                                                                        5.517045      1.650458                3.342735      0.0012

  JAN                                                                                                                                                                          2.645483      0.686784                3.851986      0.0002

  FEB                                                                                                                                                                          2.713969      0.931497                2.913555      0.0044

  MAR                                                                                                                                                                          1.816744      1.034518                1.756126      0.0821

  APR                                                                                                                                                                          -0.394414     1.111851                -0.354736     0.7235

  MAY                                                                                                                                                                          -5.888747     1.170362                -5.031559     0.0000

  JUN                                                                                                                                                                          -7.825581     1.181619                -6.622759     0.0000

  JUL                                                                                                                                                                          -8.316177     1.150511                -7.228249     0.0000

  AUG                                                                                                                                                                          -9.801331     1.109703                -8.832392     0.0000

  SEP                                                                                                                                                                          -9.078106     1.026372                -8.844851     0.0000

  OCT                                                                                                                                                                          -6.866880     0.889741                -7.717843     0.0000

  NOV                                                                                                                                                                          -3.527988     0.661458                -5.333654     0.0000

  AR(1)                                                                                                                                                                        0.827870      0.059778                13.84912      0.0000

                                                                                                                                                                                                                                   

                                                                                                                                                                                                                                   

  R-squared                                                                                                                                                                    0.929752          Mean dependent var                39.70183

  Adjusted R-squared                                                                                                                                                           0.917233          S.D. dependent var                6.981993

  S.E. of regression                                                                                                                                                           2.008671          Akaike info                       4.377119
                                                                                                                                                                                             criterion                             

  Sum squared resid                                                                                                                                                            407.5105          Schwarz criterion                 4.818471

  Log likelihood                                                                                                                                                               -243.6271         F-statistic                       74.26481

  Durbin-Watson stat                                                                                                                                                           1.683643          Prob(F-statistic)                 0.000000

                                                                                                                                                                                                                                   

                                                                                                                                                                                                                                   

  Inverted AR Roots                                                                                                                                                                  .83                                           

                                                                                                                                                                                                                                   

                                                                                                                                                                                                                                   
  ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------- ------------- ----------------------- ------------- ----------

  ----------------------------------------------------------------------------------------------------------------------- ------------- ----------------------- ------------- ----------
  Dependent Variable: D2RCUMW-D2WHUUS                                                                                                                                         

  Method: Least Squares                                                                                                                                                       

  Date: 04/12/05 Time: 14:04                                                                                                                                                  

  Sample: 1995M01 2004M12                                                                                                                                                     

  Included observations: 120                                                                                                                                                  

  Convergence achieved after 8 iterations                                                                                                                                     

                                                                                                                                                                              

                                                                                                                                                                              

  Variable                                                                                                                Coefficient   Std. Error              t-Statistic   Prob.  

                                                                                                                                                                              

                                                                                                                                                                              

  C                                                                                                                       32.85678      1.285685                25.55585      0.0000

  D2WHUUS-D2WHUUS(-1)                                                                                                     -0.202869     0.029699                -6.830837     0.0000

  DFPSPP2(-1)-(DFPSPP2(-13)+DFPSPP2(-25)+DFPSPP2(-37)+DFPSPP2(-49))/4                                                     -0.165132     0.101774                -1.622539     0.1078

  ((QHFOHRECS_ENC\*(ZWHDENC-ZWHNENC)/ZSAJQUS)+(QHFOHRECS_WNC\*(ZWHDWNC-ZWHNWNC)/ZSAJQUS))/(QHFOHRECS_ENC+QHFOHRECS_WNC)   -0.009824     0.054975                -0.178702     0.8585

  D0001                                                                                                                   -6.691290     1.829220                -3.658002     0.0004

  D0002                                                                                                                   -9.570955     1.820329                -5.257816     0.0000

  D01ON                                                                                                                   3.106688      1.498248                2.073548      0.0407

  JAN                                                                                                                     0.086232      0.657588                0.131134      0.8959

  FEB                                                                                                                     1.049892      0.843092                1.245288      0.2159

  MAR                                                                                                                     0.695360      0.948818                0.732870      0.4653

  APR                                                                                                                     0.153108      1.015043                0.150839      0.8804

  MAY                                                                                                                     -0.991546     1.056576                -0.938452     0.3503

  JUN                                                                                                                     -1.824729     1.073492                -1.699806     0.0922

  JUL                                                                                                                     -3.926477     1.061783                -3.698002     0.0004

  AUG                                                                                                                     -4.895339     1.021992                -4.789998     0.0000

  SEP                                                                                                                     -2.882694     0.950828                -3.031771     0.0031

  OCT                                                                                                                     -1.531153     0.823812                -1.858621     0.0660

  NOV                                                                                                                     -0.516497     0.609845                -0.846931     0.3990

  AR(1)                                                                                                                   0.821673      0.057229                14.35751      0.0000

                                                                                                                                                                              

                                                                                                                                                                              

  R-squared                                                                                                               0.855969          Mean dependent var                32.62004

  Adjusted R-squared                                                                                                      0.830300          S.D. dependent var                4.515709

  S.E. of regression                                                                                                      1.860231          Akaike info                       4.223574
                                                                                                                                        criterion                             

  Sum squared resid                                                                                                       349.5064          Schwarz criterion                 4.664927

  Log likelihood                                                                                                          -234.4144         F-statistic                       33.34651

  Durbin-Watson stat                                                                                                      1.630191          Prob(F-statistic)                 0.000000

                                                                                                                                                                              

                                                                                                                                                                              

  Inverted AR Roots                                                                                                             .82                                           

                                                                                                                                                                              

                                                                                                                                                                              
  ----------------------------------------------------------------------------------------------------------------------- ------------- ----------------------- ------------- ----------

  --------------------------------------------------------------------------------------------------------------------- ------------- ----------------------- ------------- ----------
  Dependent Variable: D2RCUWE-D2WHUUS                                                                                                                                       

  Method: Least Squares                                                                                                                                                     

  Date: 04/12/05 Time: 13:16                                                                                                                                                

  Sample: 1995M01 2004M12                                                                                                                                                   

  Included observations: 120                                                                                                                                                

  Convergence achieved after 8 iterations                                                                                                                                   

                                                                                                                                                                            

                                                                                                                                                                            

  Variable                                                                                                              Coefficient   Std. Error              t-Statistic   Prob.  

                                                                                                                                                                            

                                                                                                                                                                            

  C                                                                                                                     43.66335      2.540452                17.18724      0.0000

  D2WHUUS-D2WHUUS(-1)                                                                                                   -0.478106     0.056261                -8.498067     0.0000

  DFPSPP5(-1)-(DFPSPP5(-13)+DFPSPP5(-25)+DFPSPP5(-37)+DFPSPP5(-49))/4                                                   -1.545688     0.403968                -3.826259     0.0002

  ((QHFOHRECS_MTN\*(ZWHDMTN-ZWHNMTN)/ZSAJQUS+QHFOHRECS_PAC\*(ZWHDPAC-ZWHNPAC)/ZSAJQUS))/(QHFOHRECS_MTN+QHFOHRECS_PAC)   -0.003799     0.202598                -0.018753     0.9851

  JAN                                                                                                                   -1.378379     1.243021                -1.108894     0.2700

  FEB                                                                                                                   0.044998      1.654409                0.027199      0.9784

  MAR                                                                                                                   5.380943      1.886169                2.852843      0.0052

  APR                                                                                                                   6.566783      2.053559                3.197757      0.0018

  MAY                                                                                                                   6.640555      2.133635                3.112320      0.0024

  JUN                                                                                                                   5.904505      2.160541                2.732882      0.0074

  JUL                                                                                                                   4.767943      2.139431                2.228603      0.0280

  AUG                                                                                                                   0.860757      2.071924                0.415439      0.6787

  SEP                                                                                                                   1.338924      1.927310                0.694711      0.4888

  OCT                                                                                                                   1.853229      1.666609                1.111976      0.2687

  NOV                                                                                                                   1.654230      1.234337                1.340177      0.1831

  AR(1)                                                                                                                 0.843476      0.052821                15.96849      0.0000

                                                                                                                                                                            

                                                                                                                                                                            

  R-squared                                                                                                             0.818465          Mean dependent var                46.00331

  Adjusted R-squared                                                                                                    0.792282          S.D. dependent var                8.272987

  S.E. of regression                                                                                                    3.770507          Akaike info                       5.615862
                                                                                                                                      criterion                             

  Sum squared resid                                                                                                     1478.540          Schwarz criterion                 5.987528

  Log likelihood                                                                                                        -320.9517         F-statistic                       31.25940

  Durbin-Watson stat                                                                                                    2.152979          Prob(F-statistic)                 0.000000

                                                                                                                                                                            

                                                                                                                                                                            

  Inverted AR Roots                                                                                                           .84                                           

                                                                                                                                                                            

                                                                                                                                                                            
  --------------------------------------------------------------------------------------------------------------------- ------------- ----------------------- ------------- ----------

  -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- ------------- ----------------------- ------------- -----------
  Dependent Variable: DFPSP1BLD                                                                                                                                                                                                  

  Method: Least Squares                                                                                                                                                                                                          

  Date: 03/04/05 Time: 13:08                                                                                                                                                                                                     

  Sample: 1995M01 2004M10                                                                                                                                                                                                        

  Included observations: 118                                                                                                                                                                                                     

                                                                                                                                                                                                                                 

                                                                                                                                                                                                                                 

  Variable                                                                                                                                                                   Coefficient   Std. Error              t-Statistic   Prob.  

                                                                                                                                                                                                                                 

                                                                                                                                                                                                                                 

  C                                                                                                                                                                          -3.914451     1.284944                -3.046397     0.0030

  (QHFOHRECS_NEC\*(ZWHDPNE-ZWHNPNE)/ZSAJQUS+QHFOHRECS_MAC\*(ZWHDPMA-ZWHNPMA)/ZSAJQUS+QHFOHRECS_SAC\*(ZWHDPSA-ZWHNPSA)/ZSAJQUS)/(QHFOHRECS_NEC+QHFOHRECS_MAC+QHFOHRECS_SAC)   -0.588092     0.112583                -5.223652     0.0000

  D2WHUUS-100\*WTIPUUS/42                                                                                                                                                    0.109496      0.078420                1.396277      0.1657

  DFPSPP1(-1)-((DFPSPP1(-13)+DFPSPP1(-25)+DFPSPP1(-37)+DFPSPP1(-49))/4)                                                                                                      -0.047413     0.031409                -1.509503     0.1343

  D9912+D0001                                                                                                                                                                -11.19501     2.050907                -5.458566     0.0000

  D0101                                                                                                                                                                      11.65834      2.933670                3.973977      0.0001

  D0403                                                                                                                                                                      -9.279368     2.889905                -3.210960     0.0018

  JAN                                                                                                                                                                        -6.014468     1.290463                -4.660705     0.0000

  FEB                                                                                                                                                                        -3.885763     1.283475                -3.027533     0.0031

  MAR                                                                                                                                                                        -2.963090     1.315271                -2.252836     0.0265

  APR                                                                                                                                                                        2.562363      1.285106                1.993892      0.0489

  MAY                                                                                                                                                                        7.127677      1.304249                5.464966      0.0000

  JUN                                                                                                                                                                        6.983214      1.325690                5.267608      0.0000

  JUL                                                                                                                                                                        8.962513      1.319985                6.789859      0.0000

  AUG                                                                                                                                                                        6.755870      1.296618                5.210380      0.0000

  SEP                                                                                                                                                                        3.673321      1.276561                2.877514      0.0049

  OCT                                                                                                                                                                        3.534851      1.277317                2.767403      0.0067

  NOV                                                                                                                                                                        3.412772      1.310116                2.604938      0.0106

                                                                                                                                                                                                                                 

                                                                                                                                                                                                                                 

  R-squared                                                                                                                                                                  0.803901          Mean dependent var                -0.152008

  Adjusted R-squared                                                                                                                                                         0.770565          S.D. dependent var                5.704439

  S.E. of regression                                                                                                                                                         2.732392          Akaike info                       4.987802
                                                                                                                                                                                           criterion                             

  Sum squared resid                                                                                                                                                          746.5968          Schwarz criterion                 5.410449

  Log likelihood                                                                                                                                                             -276.2803         F-statistic                       24.11457

  Durbin-Watson stat                                                                                                                                                         1.865065          Prob(F-statistic)                 0.000000

                                                                                                                                                                                                                                 

                                                                                                                                                                                                                                 
  -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- ------------- ----------------------- ------------- -----------

  ----------------------------------------------------------------------------------------------------------------------- ------------- ----------------------- ------------- -----------
  Dependent Variable: DFPSP2BLD                                                                                                                                               

  Method: Least Squares                                                                                                                                                       

  Date: 03/04/05 Time: 13:09                                                                                                                                                  

  Sample: 1995M01 2004M10                                                                                                                                                     

  Included observations: 118                                                                                                                                                  

                                                                                                                                                                              

                                                                                                                                                                              

  Variable                                                                                                                Coefficient   Std. Error              t-Statistic   Prob.  

                                                                                                                                                                              

                                                                                                                                                                              

  C                                                                                                                       2.247631      0.592508                3.793423      0.0003

  ((QHFOHRECS_ENC\*(ZWHDENC-ZWHNENC)/ZSAJQUS)+(QHFOHRECS_WNC\*(ZWHDWNC-ZWHNWNC)/ZSAJQUS))/(QHFOHRECS_ENC+QHFOHRECS_WNC)   -0.149711     0.044396                -3.372169     0.0011

  D2WHUUS-100\*WTIPUUS/42                                                                                                 -0.064760     0.032769                -1.976281     0.0508

  DFPSPP2(-1)-((DFPSPP2(-13)+DFPSPP2(-25)+DFPSPP2(-37)+DFPSPP2(-49))/4)                                                   -0.347769     0.065745                -5.289691     0.0000

  JAN                                                                                                                     -2.902909     0.638210                -4.548514     0.0000

  FEB                                                                                                                     -2.629973     0.640199                -4.108054     0.0001

  MAR                                                                                                                     -3.255152     0.643807                -5.056101     0.0000

  APR                                                                                                                     -1.112803     0.639720                -1.739515     0.0849

  MAY                                                                                                                     -1.148256     0.648412                -1.770873     0.0795

  JUN                                                                                                                     -1.605125     0.655945                -2.447041     0.0161

  JUL                                                                                                                     -1.226719     0.652699                -1.879455     0.0630

  AUG                                                                                                                     -1.537953     0.643854                -2.388667     0.0187

  SEP                                                                                                                     -1.896048     0.638174                -2.971054     0.0037

  OCT                                                                                                                     -4.893741     0.638906                -7.659563     0.0000

  NOV                                                                                                                     0.711292      0.651619                1.091577      0.2776

                                                                                                                                                                              

                                                                                                                                                                              

  R-squared                                                                                                               0.634277          Mean dependent var                -0.069890

  Adjusted R-squared                                                                                                      0.584567          S.D. dependent var                2.144460

  S.E. of regression                                                                                                      1.382193          Akaike info                       3.603501
                                                                                                                                        criterion                             

  Sum squared resid                                                                                                       196.7771          Schwarz criterion                 3.955707

  Log likelihood                                                                                                          -197.6066         F-statistic                       12.75955

  Durbin-Watson stat                                                                                                      2.059011          Prob(F-statistic)                 0.000000

                                                                                                                                                                              

                                                                                                                                                                              
  ----------------------------------------------------------------------------------------------------------------------- ------------- ----------------------- ------------- -----------

  ----------------------------------------------------------------------------------------------------------------------- ------------- ----------------------- ------------- -----------
  Dependent Variable: DFPSP3BLD                                                                                                                                               

  Method: Least Squares                                                                                                                                                       

  Date: 03/04/05 Time: 13:10                                                                                                                                                  

  Sample: 1995M01 2004M10                                                                                                                                                     

  Included observations: 118                                                                                                                                                  

                                                                                                                                                                              

                                                                                                                                                                              

  Variable                                                                                                                Coefficient   Std. Error              t-Statistic   Prob.  

                                                                                                                                                                              

                                                                                                                                                                              

  C                                                                                                                       0.420274      0.626466                0.670865      0.5038

  ((QHFOHRECS_ESC\*(ZWHDESC-ZWHNESC)/ZSAJQUS)+(QHFOHRECS_WSC\*(ZWHDWSC-ZWHNWSC)/ZSAJQUS))/(QHFOHRECS_ESC+QHFOHRECS_WSC)   -0.047351     0.064813                -0.730583     0.4667

  D2WHUUS-100\*WTIPUUS/42                                                                                                 -0.032486     0.033128                -0.980593     0.3291

  DFPSPP3(-1)-((DFPSPP3(-13)+DFPSPP3(-25)+DFPSPP3(-37)+DFPSPP3(-49))/4)                                                   -0.307164     0.069365                -4.428200     0.0000

  JAN                                                                                                                     -1.964362     0.694228                -2.829561     0.0056

  FEB                                                                                                                     -1.265637     0.697269                -1.815135     0.0724

  MAR                                                                                                                     0.530943      0.688942                0.770664      0.4427

  APR                                                                                                                     0.112539      0.689892                0.163126      0.8707

  MAY                                                                                                                     1.246573      0.697545                1.787087      0.0769

  JUN                                                                                                                     0.365202      0.702070                0.520179      0.6041

  JUL                                                                                                                     1.067372      0.699036                1.526919      0.1298

  AUG                                                                                                                     -0.117275     0.691811                -0.169519     0.8657

  SEP                                                                                                                     0.515311      0.687056                0.750027      0.4549

  OCT                                                                                                                     -0.260876     0.694592                -0.375581     0.7080

  NOV                                                                                                                     0.715921      0.703436                1.017748      0.3112

                                                                                                                                                                              

                                                                                                                                                                              

  R-squared                                                                                                               0.372281          Mean dependent var                -0.000144

  Adjusted R-squared                                                                                                      0.286960          S.D. dependent var                1.762274

  S.E. of regression                                                                                                      1.488094          Akaike info                       3.751151
                                                                                                                                        criterion                             

  Sum squared resid                                                                                                       228.0857          Schwarz criterion                 4.103357

  Log likelihood                                                                                                          -206.3179         F-statistic                       4.363292

  Durbin-Watson stat                                                                                                      2.105765          Prob(F-statistic)                 0.000005

                                                                                                                                                                              

                                                                                                                                                                              
  ----------------------------------------------------------------------------------------------------------------------- ------------- ----------------------- ------------- -----------

  ----------------------------------------------------------------------- ------------- ----------------------- ------------- -----------
  Dependent Variable: DFPSP4BLD                                                                                               

  Method: Least Squares                                                                                                       

  Date: 03/04/05 Time: 13:12                                                                                                  

  Sample: 1995M01 2004M10                                                                                                     

  Included observations: 118                                                                                                  

                                                                                                                              

                                                                                                                              

  Variable                                                                Coefficient   Std. Error              t-Statistic   Prob.  

                                                                                                                              

                                                                                                                              

  C                                                                       0.299666      0.092707                3.232398      0.0016

  (ZWHDMTN-ZWHNMTN)/ZSAJQUS                                               -0.006084     0.010254                -0.593302     0.5543

  D2WHUUS-100\*WTIPUUS/42                                                 0.000686      0.004575                0.149982      0.8811

  DFPSPP4(-1)-((DFPSPP4(-13)+DFPSPP4(-25)+DFPSPP4(-37)+DFPSPP4(-49))/4)   -0.402844     0.074263                -5.424583     0.0000

  JAN                                                                     -0.328174     0.101411                -3.236074     0.0016

  FEB                                                                     -0.456198     0.101154                -4.509951     0.0000

  MAR                                                                     -0.413176     0.101754                -4.060547     0.0001

  APR                                                                     -0.542954     0.101764                -5.335403     0.0000

  MAY                                                                     0.087368      0.102794                0.849937      0.3973

  JUN                                                                     -0.099759     0.104778                -0.952098     0.3433

  JUL                                                                     -0.386202     0.103840                -3.719215     0.0003

  AUG                                                                     -0.653875     0.102618                -6.371944     0.0000

  SEP                                                                     -0.223575     0.101124                -2.210898     0.0293

  OCT                                                                     -0.256147     0.100958                -2.537170     0.0127

  NOV                                                                     0.161679      0.103517                1.561866      0.1214

                                                                                                                              

                                                                                                                              

  R-squared                                                               0.620916          Mean dependent var                -0.004246

  Adjusted R-squared                                                      0.569390          S.D. dependent var                0.334522

  S.E. of regression                                                      0.219516          Akaike info                       -0.076498
                                                                                        criterion                             

  Sum squared resid                                                       4.963307          Schwarz criterion                 0.275708

  Log likelihood                                                          19.51338          F-statistic                       12.05052

  Durbin-Watson stat                                                      2.095271          Prob(F-statistic)                 0.000000

                                                                                                                              

                                                                                                                              
  ----------------------------------------------------------------------- ------------- ----------------------- ------------- -----------

  ----------------------------------------------------------------------- ------------- ----------------------- ------------- -----------
  Dependent Variable: DFPSP5BLD                                                                                               

  Method: Least Squares                                                                                                       

  Date: 03/04/05 Time: 13:13                                                                                                  

  Sample: 1995M01 2004M10                                                                                                     

  Included observations: 118                                                                                                  

                                                                                                                              

                                                                                                                              

  Variable                                                                Coefficient   Std. Error              t-Statistic   Prob.  

                                                                                                                              

                                                                                                                              

  C                                                                       0.326230      0.314954                1.035802      0.3027

  (ZWHDPAC-ZWHNPAC)/ZSAJQUS                                               -0.035119     0.049709                -0.706493     0.4815

  D2WHUUS-100\*WTIPUUS/42                                                 0.012257      0.015842                0.773669      0.4409

  DFPSPP5(-1)-((DFPSPP5(-13)+DFPSPP5(-25)+DFPSPP5(-37)+DFPSPP5(-49))/4)   -0.408989     0.077418                -5.282861     0.0000

  JAN                                                                     -0.849506     0.347694                -2.443256     0.0163

  FEB                                                                     -0.994810     0.354123                -2.809220     0.0059

  MAR                                                                     -0.582874     0.349641                -1.667064     0.0985

  APR                                                                     0.133777      0.353363                0.378584      0.7058

  MAY                                                                     -0.159763     0.354806                -0.450283     0.6535

  JUN                                                                     -0.953231     0.358000                -2.662654     0.0090

  JUL                                                                     -0.725404     0.356657                -2.033901     0.0445

  AUG                                                                     -0.459536     0.353040                -1.301652     0.1959

  SEP                                                                     -0.161993     0.351955                -0.460267     0.6463

  OCT                                                                     -0.272402     0.352375                -0.773046     0.4413

  NOV                                                                     0.017192      0.357471                0.048093      0.9617

                                                                                                                              

                                                                                                                              

  R-squared                                                               0.378365          Mean dependent var                -0.005534

  Adjusted R-squared                                                      0.293871          S.D. dependent var                0.897649

  S.E. of regression                                                      0.754307          Akaike info                       2.392248
                                                                                        criterion                             

  Sum squared resid                                                       58.60490          Schwarz criterion                 2.744454

  Log likelihood                                                          -126.1426         F-statistic                       4.478010

  Durbin-Watson stat                                                      2.136394          Prob(F-statistic)                 0.000003

                                                                                                                              

                                                                                                                              
  ----------------------------------------------------------------------- ------------- ----------------------- ------------- -----------

[^1]: These assumptions were tested in estimation of alternative models
    with different model structural specifications and different crude
    oil prices, such as the U.S. refinery average acquisition cost.