DOE/EIA-0626

February 1999

Price Changes in the

Gasoline Market

Are Midwestern Gasoline Prices

Downward Sticky?

Energy Information Administration

Washington, DC 20585

This report was prepared by the Energy Information Administration, the independent statistical and

analytical agency within the Department of Energy. The information contained herein should not be

construed as advocating or reflecting any policy position of the Department of Energy or of any

other organization.
Contacts and Acknowledgments

This report was prepared by the Energy Information Administration (EIA) under the direction of

Dr. John Cook, Director, Petroleum Division, Office of Oil and Gas, (202) 586-5986,

jcook@eia.doe.gov, and Craig Cranston, Team Leader, Petroleum Analysis Team, (202) 586-6023,

ccransto@eia.doe.gov. Questions about this report can be directed to:

Dr. Charles Dale (202) 586-1805, cdale@eia.doe.gov

John Zyren (202) 586-6405, jzyren@eia.doe.gov

Dr. Robert Manicke (202) 586-7611, rmanicke@eia.doe.gov

Michael Burdette (202) 586-6649, mburdett@eia.doe.gov

EIA would like to acknowledge the contributions of Jake Bournazian, Charles Riner, Nancy

Masterson, Carolyn Mack and Cynthia McLaughlin of the Office of Oil and Gas in preparing this

report. In addition, EIA would like to thank the following contract employees for their assistance:

Jamie Chan, Professor Robert Trost and Joanne Shore.

ii Energy Information Administration/Price Changes in the Gasoline Market
Contents

Executive Summary . . . . . . . . . . . . . . . . . . . . . . . .................1

1. Introduction..........................................3

2. Previous Studies of Gasoline Price Movements Produced Conflicting Results . . . . . . . 9

3. The Energy Information Administration’s Study of Gasoline Price Changes . . . . . . . 17

Appendix A. A Moving Average / Symmetry Primer . . . . . . . . . . . . . . . . . . . . . . 29

Appendix B. Quantitative Explanation of Motor Gasoline Market Price Passthrough . . . . . 35

Appendix C. Methodology for Asymmetry Testing. . . . . . . . . . . . . . . . . . . . . . . 45

Appendix D. Derivation of Pattern Asymmetry Test Equation . . . . . . . . . . . . . . . . . 55

Tables

3.1 Gasoline Price Levels Move Together, Creating Statistical Headaches . . . . . . . . . . 22

3.2 Gasoline Price Changes Have Less Concurrent Correlation Than Levels . . . . . . . . . 23

3.3 Statistical Tests Show “Pattern Asymmetry” of PADD 2 Price Changes

at the Retail Level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

3.4 Price Passthrough Analysis to Midwest Retail Prices . . . . . . . . . . . . . . . . . . . 25

3.5 Price Passthrough from West Texas Intermediate Crude Oil . . . . . . . . . . . . . . . . 25

3.6 Wholesale Price Passthrough. Levels and Differences Produce Stable Results . . . . . . 26

3.7 Statistical Tests Show Widespread “Amount Symmetry” of Price Changes . . . . . . . . 26

B.1 OLS Regression Results for Retail Price Passthrough . . . . . . . . . . . . . . . . . . . 38

B.2 OLS Regression Results for Price Passthrough from WTI . . . . . . . . . . . . . . . . . 40

B.3 OLS Regression Results for Wholesale Price Passthrough . . . . . . . . . . . . . . . . . 42

C.1 OLS Test Equation for Retail Pattern Asymmetry . . . . . . . . . . . . . . . . . . . . . 48

C.2 OLS Test Equation for WTI Passthrough Pattern Asymmetry . . . . . . . . . . . . . . . 50

C.3 OLS Test Equation for Wholesale Pattern Asymmetry. . . . . . . . . . . . . . . . . . . 52

Figures

1.1 Do Gasoline Prices Rise Faster Than They Fall? . . . . . . . . . . . . . . . . . . . . . . 4

1.2 California Gasoline — A World Apart. . . . . . . . . . . . . . . . . . ..........5

1.3 Simplified Gasoline Market Structure . . . . . . . . . . . . . . . . . . ..........6

3.1 Components of Retail Regular Gasoline Prices. . . . . . . . . . . . . . . . . . . . . . . 18

3.2 Do Midwest Gasoline Prices Move With Crude Oil Prices? . . . . . . . . . . . . . . . . 19

3.3 Crude Oil Prices and Spot Gasoline Prices Move Together . . . . . . . . . . . . . . . . 19

3.4 Refinery and Pipeline Spot Prices Are Closely Correlated . . . . . . . . . . . . . . . . . 20

3.5 Pipeline Spot and Rack Prices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

3.6 Rack Prices and Retail Prices. A Source of Asymmetry? . . . . . . . . . . . . . . . . . 21

3.7 Lagged Wholesale Prices Predict Retail Prices at the National Level . . . . . . . . . . . 21

3.8 Lags Between Spot and Retail Prices Can Create Pattern Asymmetry . . . . . . . . . . . 27

A.1 Example of Amount Symmetry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

A.2 Example of Pattern Symmetry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

A.3 Example of Pattern Asymmetry Caused by a Lagged Response . . . . . . . . . . . . . . 31

A.4 Actual and Forecasted Midwest Retail Gasoline Prices . . . . . . . . . . . . . . . . . . 32

A.5 Gulf Coast Spot and Midwest Retail Gasoline Prices . . . . . . . . . . . . . . . . . . . 33

Energy Information Administration/Price Changes in the Gasoline Market iii
Executive Summary

Price asymmetry is the phenomenon whereby prices tend to move differently depending on their

direction. To the public, with regard to gasoline, this typically means that retail prices rise faster

than they fall. This is sometimes referred to as “downward sticky” prices. Richard Blumenthal, the

Connecticut attorney general, was quoted in the December 7, 1998 Washington Post as saying the

Exxon-Mobil merger will face scrutiny from regulators because “Gas prices go up a lot faster than

they go down.”

EIA data confirm the notion that retail gasoline prices appear asymmetric, typically rising more

quickly than they fall. However, there is significantly more to the question of price asymmetry than

just the upward and downward speed of retail price movements. For the most part, retail prices

move in response to changes in wholesale (or even raw material) prices further upstream in the

manufacturing/distribution chain. Therefore, an examination of price asymmetry must consider the

speed and degree to which price changes at one level are passed downstream, i.e. from wholesale

toward retail. Two types of price asymmetry are discussed and tested in this paper. These are

amount asymmetry, in which the magnitude of the eventual price change differs between different

market levels such as wholesale and retail, and pattern asymmetry, in which the change occurs at a

different rate between market levels depending on direction.

Previous studies of gasoline price asymmetry, by the General Accounting Office, the American

Petroleum Institute, the Federal Reserve Bank of Dallas, and others have reached differing

conclusions on the existence or nonexistence of price asymmetry in gasoline markets. This report

discusses how those different conclusions are largely the result of different types of econometric

modeling and different frequencies of data, e.g., weekly versus monthly prices.

The report concentrates on regional gasoline prices in the Midwest from October 1992 through June

1998, and reaches the following conclusions:

• Wholesale and retail gasoline price changes in the Midwest during this period are basically

symmetric with respect to changes in crude oil prices.

• Retail gasoline prices in the Midwest often rise faster than they fall in response to wholesale gasoline

price changes, so the report detected pattern asymmetry. However, after all lagged price adjustments

have been completed, wholesale price changes will almost completely pass through to the retail level,

so there is little evidence of amount asymmetry.

• The adjustment times between different levels of the gasoline market make it possible for the

detection of pattern asymmetry to be only a statistical artifact. The report shows how, because of time

lags in the gasoline distribution system, retail prices may continue to rise even after wholesale prices

have begun falling, giving the appearance of pattern price asymmetry. However, when allowance is

made for the lagged adjustment times, the perceived pattern asymmetry largely disappears.

• The conclusions of this report depend importantly on various characteristics of the data used,

including frequency and location specificity. Thus, conclusions about price asymmetry at the city or

state level would necessitate a collection of data and an examination of numerous local gasoline

markets.

Energy Information Administration/Price Changes in the Gasoline Market 1
1. Introduction

This report examines a recurring question about gasoline markets: why, especially in times of high

price volatility, do retail gasoline prices seem to rise quickly but fall back more slowly? Do

gasoline prices actually rise faster than they fall, or does this just appear to be the case because

people tend to pay more attention to prices when they’re rising? This question is more complex

than it might appear to be initially, and it has been addressed by numerous analysts in government,

academia and industry. The question is very important, because perceived problems with retail

gasoline pricing have been used in arguments for government regulation of prices.

The phenomenon of prices at different market levels tending to move differently relative to each

other depending on direction is known as price asymmetry. To the public, this typically means

simply the notion that retail prices rise faster than they fall, when observed over some period.

However, there is significantly more to the question of price asymmetry than just the upward and

downward speed of retail price movements. For the most part, retail prices move in response to

changes in wholesale (or even raw material) prices further upstream in the

manufacturing/distribution chain. Therefore, an examination of price asymmetry must consider the

speed and degree to which price changes at one level are passed downstream, i.e. from wholesale

toward retail. In previous studies of this phenomenon, researchers have further defined two types

of price asymmetry: amount asymmetry, in which the amount of the eventual price change differs

between wholesale and retail and/or between upward and downward movements, and pattern

asymmetry, in which the change occurs at a different rate between market levels depending on

direction.

This report summarizes the previous work on gasoline price asymmetry and provides a method for

testing for asymmetry in a wide variety of situations. The major finding of this paper is that there is

some amount asymmetry and pattern asymmetry, especially at the retail level, in the Midwestern

states that are the focus of the analysis. Nevertheless, both the amount asymmetry and pattern

asymmetry are relatively small. In addition, much of the pattern asymmetry detected in this and

previous studies could be a statistical artifact caused by the time lags between price changes at

different points in the gasoline distribution system. In other words, retail gasoline prices do

sometimes rise faster than they fall, but this is largely a lagged market response to an upward shock

in the underlying wholesale gasoline or crude oil prices, followed by a return toward the previous

baseline. After consistent time lags are factored out, most apparent asymmetry disappears.

Regional Price Differences. Analysis of retail gasoline pricing is complicated by large price

differences in various parts of the country. Figure 1.1 shows retail gasoline prices in Chicago,

Houston and Dallas. Not surprisingly, gasoline prices are cheaper in Texas, where stations are

closer to both production and refining. Nevertheless, the market trends both upward and downward

are fairly similar between the cities shown. It is not immediately apparent from the figure whether

or not prices rise faster than they fall. Clearly, some type of statistical testing is necessary.

While Houston and Dallas have similar gasoline prices, it doesn’t necessarily follow that intrastate

gasoline prices will be similar in most states. This is most dramatically illustrated in California, as

Energy Information Administration/Price Changes in the Gasoline Market 3
shown in Figure 1.2. Retail gasoline prices among Los Angeles, San Francisco and San Diego may

be nearly identical, as they were in spring 1996, or they might vary by nearly 20 cents per gallon, as

in early 1998. California is in a unique situation: with more stringent requirements than Federal

Government-mandated clean gasolines, it has relatively few supply sources outside the state, and it

is very vulnerable to refinery problems. These factors complicate analyses of the California

gasoline market.

The Gasoline Market. To understand the approach used in this report, first consider the gasoline

market structure shown in Figure 1.3, which shows the various market levels between spot crude oil

prices and retail gasoline prices. There are two key concepts to keep in mind when analyzing a

gasoline market: “asymmetry” and “passthrough.” As noted above, “asymmetry” refers to prices

rising and falling at differing rates at different levels in the pricing structure. However, the analysis

is complicated by the fact that there are lags between changes in upstream prices and the

corresponding changes in downstream prices. The upstream price changes take time to “pass

through” to the downstream prices. The passthrough times make it theoretically possible for there

to be no real asymmetry in price movements, but because of the time lags a statistical test may show

that there is asymmetry.

4 Energy Information Administration/Price Changes in the Gasoline Market

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1.80

2.00

12/19/94

2/19/954/19/956/19/958/19/95

10/19/9512/19/95

2/19/964/19/966/19/968/19/96

10/19/9612/19/96

2/19/974/19/976/19/978/19/97

10/19/9712/19/97

2/19/984/19/986/19/98

Dollars Per Gallon

Chicago Houston Dallas

Figure 1.1 Do Gasoline Prices Rise Faster Than They Fall?

Regular Unleaded Pump Prices

Source: Energy Information Administration, Form EIA-878, “Motor Gasoline Price Survey.”
An example of apparent asymmetry due to passthrough lag times is shown in Appendix A. There it

is shown that even a very simple price change, such as a symmetrical 5 cents per gallon upstream

price increase and decrease spread over 10 weeks, can have unusual consequences because of lags

(see Figure A.3). The downstream price peaks 2 weeks after the upstream price maximum, and it

takes 4 weeks longer for the downstream price to return to equilibrium than the upstream price.

Lags anywhere in the system can therefore give the appearance that prices are sticky downward,

when in fact the apparent asymmetry is only an artifact of the lags.

Figure 1.3 shows that there are a number of points where price asymmetry may arise. Spot crude oil

prices would be affected by the supply and demand of crude oil and all petroleum products, while

refinery spot gasoline prices would be impacted by both crude oil prices and seasonal demands for

gasoline. Gasoline typically moves through the system and may also trade at spot prices at a major

pipeline delivery point. Further along the distribution system, the terminal, or “rack” price, is a

wholesale price that represents about 52 percent of refiners’ gasoline sales,1 so those prices also are

a prominent part of any analysis of price asymmetry.

Energy Information Administration/Price Changes in the Gasoline Market 5

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1.80

2.00

12/19/94

2/19/954/19/956/19/958/19/95

10/19/9512/19/95

2/19/964/19/966/19/968/19/96

10/19/9612/19/96

2/19/974/19/976/19/978/19/97

10/19/9712/19/97

2/19/984/19/986/19/98

Dollars Per Gallon

Los Angeles San Francisco San Diego

Figure 1.2 California Gasoline -- A World Apart

Regular Unleaded Pump Prices

Source: Energy Information Administration, Form EIA-878, "Motor Gasoline Price Survey."

1 Refinery sales data are from Petroleum Marketing Monthly, Energy Information Administration,

DOE/EIA-0380(98/12) (Washington, DC, December 1998).
6 Energy Information Administration/Price Changes in the Gasoline Market

Crude Oil Spot West Texas Intermediate

Crude Oil Price

Retail Station Retail Gasoline Price

City Terminal Rack Price

Refinery Refinery Spot Gasoline Price

Pipeline Pipeline Spot Gasoline Price

Figure 1.3 Simplified Gasoline Market Structure

Source: Adapted from John Zyren, “What Drives Gasoline Prices?,” Petroleum Marketing Monthly, Energy Information

Administration, DOE/EIA-0380 (Washington, DC, June 1995), p. xiv.
There is also an additional level not shown on Figure 1.3, the dealer tank wagon (DTW) level,

which would represent sales from refiners or marketers to retail stations. This represents about 20

percent of refiners’ direct sales, and an additional quantity of resales by marketers. That level is

omitted here because of problems with the availability and consistency of DTW prices. Finally, the

retail price reflects all costs, taxes and profits through the system. Since retail prices are the ones

most important to the consumer, they are the primary focus of this report.

Figure 1.3 can also be used to illustrate two terms used throughout this report: “upstream” and

“downstream” prices. These terms are used to distinguish the relative position of two prices in the

distribution system. Upstream prices are commodity prices closer to the production point (top of

Figure 1.3) relative to downstream prices which are motor gasoline prices closer to the final

consumption point (bottom of Figure 1.3). For example, retail motor gasoline prices are

downstream relative to all other commodity prices because they constitute the price the final

consumer (motorist) pays. Also, pipeline spot gasoline prices are downstream from U.S. Gulf

Coast (USG) spot gasoline prices because they are closer to the consumer. Conversely, USG spot

prices are upstream relative to pipeline spot prices because the USG spot prices are further removed

from the final consumption of gasoline and are closer to the production side. Crude oil spot prices

are upstream of all motor gasoline prices because they are farthest from the final consumer.

Given the simplified market structure shown in Figure 1.3, the decision must be made on where to

concentrate the analysis. Since the Energy Information Administration (EIA) had already

examined gasoline prices,2 and detected unusually rapid price passthrough from upstream to

downstream prices in the Midwest relative to the rest of the country, this study concentrated on the

Midwest. The results, therefore, cannot necessarily be generalized to the rest of the country. In

addition, this work was undertaken to determine whether all the numerous problems associated

with analyzing price asymmetry could be appropriately modeled. Once the modeling concept has

been shown to be workable for the Midwestern region, it could be applied on a case-by-case basis to

other regions.

Chapter 2 reviews the numerous previous studies of gasoline price asymmetry and discusses their

frequently contradictory results. Chapter 3 describes the EIA’s approach to testing for asymmetry

in Midwest gasoline prices. The EIA approach also resolves some of the reported earlier problems

that produced unexplainable contradictory results. Technical details of the methods used are in the

Appendices.

Energy Information Administration/Price Changes in the Gasoline Market 7

2 See Assessment of Summer 1997 Motor Gasoline Price Increase, Energy Information Administration, DOE/EIA-0621

(Washington, DC, May 1998).
2. Previous Studies of Gasoline Price

Movements Produced Conflicting Results

The question of whether prices rise and fall at different speeds is not unique to gasoline, and is not

by any means a recent concept. Economists have examined relative price movements for decades,

and studies of gasoline prices stretch back over 30 years. For purposes of the current study, only

those previous studies specifically examining price asymmetry were reviewed. The methods and

results of eight such studies are presented here.

Robert W. Bacon. Concern about gasoline price movements is not confined to the United States.

In Britain, a government commission reported on gasoline price movements 3 times over the

25-year period from 1965 to 1990. Robert Bacon1 described their conclusions as comparing

gasoline price rises to rockets and price drops to feathers, and he made an early attempt to measure

any price asymmetries statistically.

The gasoline market in Britain is very different from the market in the United States. For example,

in Europe the major spot market for gasoline, and therefore the best data source, is Rotterdam,

where it is priced in U.S. dollars. So, in his analysis Bacon had to contend with dollar/British pound

exchange rate changes. Nevertheless, much of his analysis is applicable to the U.S. market.

The data source and frequency are important considerations, since any price adjustments occurring

faster than the data is sampled will be not be measured properly. Bacon had retail data available

only biweekly, from June 1982 through January 1990. Given the available data, the next question is

how to model price asymmetry.

A possible modeling approach is to divide the data series into two parts, one in which there are

rising prices and the other in which there are falling prices. This is called a “partitioning” or

“switching regimes” model. The rising and falling periods may then be compared econometrically.

The difficulty with this approach is that there may be long lags between a cost change before the full

effects are passed on through the distribution system. Thus, it is extremely difficult to determine the

exact switching points between periods of rising and falling prices unless there is a good prior

knowledge of the lag structure. However, determining the lag structure is one of the objectives of

the modeling itself, so this technique is very difficult to use.

A second possible modeling approach is to assume that sellers at each step of the distribution chain

shown in Figure 1.3 attempt to set their target prices at a particular level reflecting their current

costs plus a markup. The difference between the prices they actually receive at a given time and

their target prices will result in their adjusting their prices toward their target prices. The speed of

price adjustment in the upward and downward direction can be measured, to determine if the price

changes occur asymmetrically. This is called an “adjustment” model, and is the method Bacon used

to test for price asymmetry.

Energy Information Administration/Price Changes in the Gasoline Market 9

1 Bacon, R.W., “Rockets and Feathers: The Asymmetric Speed of Adjustment of UK Retail Gasoline Prices to Cost

Changes,” Energy Economics (July 1991), pp. 211-218.
Bacon concluded from his model that all price changes are eventually fully passed on to the

consumer. However, there was a high degree of asymmetry in gasoline prices in Britain, since retail

prices rise much faster than they fall in response to cost changes. However, like the government

reports before him, he also concluded that the asymmetry was so short-lived that British gasoline

markets could still be considered highly competitive.

Bacon’s conclusions may be summarized as follows:

• Price asymmetry is difficult to define and model, but it is not an impossible task.

• The shorter the time period between data points, the stronger the conclusions that may be drawn

from the analysis.

• Using a price adjustment model, he concluded that there was strong evidence for gasoline price

asymmetry in Britain between 1982 and 1990. But, whenever asymmetrical price movements

occurred they were short-term in duration, so he couldn’t reject the proposition that British

gasoline markets are highly competitive.

The rest of the studies summarized below all analyzed U.S. gasoline markets.

Jeffrey D. Karrenbrock. The U.S. gasoline market has several levels where price asymmetry can

occur, a point discussed thoroughly by Karrenbrock.2 He noted that consumers routinely blame

quickly rising gasoline prices at the pump on oil companies, but only about 19 percent of refiners’

gasoline sales go directly to company owned stations and other end users, with the other 81 percent

getting sold either to independent dealers or to jobbers who resell it to gas stations. Thus, there are

various ways for asymmetric price movements to arise.

Karrenbrock distinguishes between 3 different types of possible price asymmetries. First, there is

“pattern asymmetry,” which is the focus of this report and most other asymmetry studies. Pattern

asymmetry simply means that an increase in price is passed through from an upstream price, such as

a wholesale price to a retail price, faster than a decrease in price is passed through from a wholesale

to a retail price. He also defined the term “amount asymmetry,” to mean whether total retail price

changes either upward or downward might not ever completely reflect wholesale price changes,

regardless of the timing involved. Finally, he defined a “combination asymmetry” that would

reflect both pattern and amount price differences.

Unlike Bacon, Karrenbrock used a partitioning model instead of an adjustment model, because he

needed a partitioning model to test all his asymmetry hypotheses. He notes that price asymmetry

had been found in studies of markets in fruits, vegetables, and meats, so he wouldn’t be surprised to

find it in gasoline markets.

He used monthly data from January 1983 through December 1990. Since he did his study in 1991,

he was using up-to-date data. For his starting point, he noted that oil markets were decontrolled by

September 1981, so he began using data from a little over a year later, to allow for the complete

adoption of any new gasoline marketing procedures.

10 Energy Information Administration/Price Changes in the Gasoline Market

2 Karrenbrock, J. D., “The Behavior of Retail Gasoline Prices: Symmetric or Not?,” Federal Reserve Bank of St. Louis

Review (July/August 1991), pp. 19-29.
His partitioning model found that there was pattern asymmetry. Wholesale price increases are

initially passed through to the retail level much faster than wholesale price decreases are passed

through to the retail level. However, there was no “amount asymmetry” from wholesale prices to

retail gasoline prices, because after about two months all wholesale price increases or decreases

were completely reflected at the retail level. Thus, in spite of initial fast retail price rises, in the long

run consumers are harmed very minimally by “pattern asymmetry.”

Karrenbrock’s report may be summarized as follows:

• He used a partitioning model to analyze monthly data from January 1983 through December

1990, and concluded that in response to wholesale price changes, retail gasoline prices rise faster

than they fall.

• If wholesale gasoline prices rise 10 cents per gallon in one month, retail gasoline prices will rise

6.8 cents in the same month and 3.5 cents in the following month. If wholesale gasoline prices

instead fall 10 cents per gallon in one month, retail gasoline prices will fall only 3.0 cents in the

same month but will then fall 6.9 cents in the following month.

• In spite of the asymmetric price movements, the total length of time for complete retail price

passthrough is about the same (2 months) whether wholesale prices are rising or falling, and

complete price passthrough is achieved in both cases, so the ultimate effect of the asymmetry on

retail consumers is small.

The American Petroleum Institute. In response to attempts to legislate price controls,3 the

American Petroleum Institute (API) analyzed gasoline markets in two separate studies. First,

Norman and Shin4 (hereafter NS) applied Bacon’s adjustment model to gasoline prices at several

places in the gasoline distribution system. They used monthly data from January 1982 through

December 1990 to examine the relationship between crude oil prices, wholesale prices charged by

refiners, and average retail gasoline prices. They also used weekly price series of West Texas

Intermediate spot prices, New York wholesale gasoline prices, and average weekly retail prices for

gasoline.

NS applied their adjustment model to both their API weekly and monthly data series and concluded

that retail gasoline markets move symmetrically with respect to both crude oil prices and wholesale

gasoline prices. This result was very different from the earlier findings of Bacon in the United

Kingdom and Karrenbrock’s concurrent study of U.S. markets.

The other American Petroleum Institute study was done by Shin.5 He reviewed the previous

literature and concluded that different price asymmetry conclusions might arise not only because of

the different types of models being used, such as adjustment models versus partitioning models, but

also because of the different data series being used in the studies.

Energy Information Administration/Price Changes in the Gasoline Market 11

3 For example, in 1990 the Senate considered a bill, which ultimately did not pass, called the “National Emergency

Anti-Profiteering Act of 1990,” which could have given the President the power to put price controls on gasoline.

4 Norman, D. A. and Shin, D., “Price Adjustment in Gasoline and Heating Oil Markets,” American Petroleum Institute,

Research Study #060 (Washington, DC, August 1991).

5 Shin, D., “Do Product Prices Respond Symmetrically to Changes in Crude Prices?,” American Petroleum Institute

Research Study #068 (Washington, DC, December 1992).
Shin applied the data series used in the previous studies to the different models used. When he

combined Karrenbrock’s data and the NS model, the NS model results changed from indicating

price symmetry to indicating price asymmetry. But when he combined the NS data with

Karrenbrock’s model, the Karrenbrock model results changed from indicating price asymmetry to

indicating price symmetry. This was a dramatic illustration of how choice of data series can affect

the results.

Shin then tested an adjustment model, using monthly Energy Information Administration data from

January of 1986 through May of 1992. The data series was available from 1982, but he wanted to

avoid structural market changes from many small refineries going out of business in the early

1980’s. For changes from crude oil prices to wholesale gasoline prices, he concluded that there was

no statistical evidence of price asymmetry. However, if asymmetry did exist, the asymmetry was

that wholesale prices fall faster than they rise in response to crude oil price changes, a result

opposite to the conventional wisdom. For gasoline prices at the retail level, he concluded only that

results of various asymmetry tests were mixed.

The results of the American Petroleum Institute reports are summarized here:

• Norman and Shin used an adjustment model and concluded that retail gasoline prices do not

show any evidence of asymmetry, even at data intervals as short as one week. Theirs is one of the

few studies that do not have ambiguous conclusions. They conclude that prices in retail gasoline

markets move symmetrically. They attribute their differences from Bacon’s results in the United

Kingdom to the fact that the U.S. gasoline market is more highly competitive.

• Shin emphasized the importance of data in price asymmetry studies. He used Karrenbrock’s and

Norman and Shin’s data series in each other’s models, and reversed the results of both studies.

Thus, Norman and Shin’s unambiguous results discussed above suddenly became less certain.

Shin also estimated another adjustment model using Energy Information Administration data.

He concluded that wholesale prices actually fall faster than they rise, but that the tests for

gasoline price asymmetry at the retail level are still inconclusive.

The U.S. General Accounting Office. In March 1993, in response to numerous requests from the

Congress, the U.S. General Accounting Office (hereafter GAO) issued a report on gasoline

pricing.6 The GAO was especially interested in gasoline price adjustments during a period of

shocks, since most of the Congressional interest was the result of the Persian Gulf War in 1990 and

1991.

The GAO began by interviewing experts in the petroleum industry. The experts noted that the

existence of viable futures markets in crude oil and gasoline on the New York Mercantile Exchange

made it possible for rapid price changes in crude oil prices to be reflected very quickly in spot

gasoline prices. The immediacy of futures price changes gave sophisticated buyers information

that can be quickly used when buying and selling wholesale gasoline. Retail prices were another

matter. All but a few of the industry experts concluded that there are probably some asymmetric

price movements between wholesale and retail gasoline price movements.

12 Energy Information Administration/Price Changes in the Gasoline Market

6 U.S. General Accounting Office, “Energy Security and Policy: Analysis of the Pricing of Crude Oil and Petroleum

Products,” Report GAO/RCED-93-17, U.S. Government Printing Office (Washington, DC, March 1993).
The GAO then reviewed the gasoline price asymmetry literature. Most of the work they reviewed is

summarized above. Since that work gave conflicting results on the existence of gasoline price

asymmetry, they decided to construct their own model.

The GAO built a partitioning model, expressly to analyze periods of market price shocks. They

defined a market shock as a rise in the price of crude oil of 8.25 percent in 1 week, a value obtained

from a statistical analysis of their data series, which was weekly data from January 1984 to March

1991. Unlike prior studies, they also constructed their model to incorporate crude oil stocks,

gasoline stocks, and refinery utilization rates, all of which could be especially important in a period

following a market shock. Theoretically, including these additional important variables in their

model should make their asymmetry estimates more reliable.

The GAO model found price asymmetry during market shocks, but only in the price of wholesale

gasoline in response to a change in the crude oil price. They concluded that crude oil price increases

would be reflected in wholesale gasoline prices within 1 week, the smallest interval that the

frequency of their data would permit. However, they found that there was no price asymmetry at

the level from wholesale to retail gasoline prices, and that at the retail level the price adjustment

could last as long as 17 weeks. Their results, concluding that retail gasoline prices move up and

down symmetrically in response to wholesale price movements, contradicted all prior studies

except the Norman and Shin study.

The results of the GAO study may therefore be summarized:

• The GAO used a partitioning model to analyze market shocks. Analyzing weekly data from

January 1984 to March 1991, they concluded that wholesale gasoline prices rise faster than they

fall with respect to crude oil prices. For every 10 cent per gallon rise in the spot price of crude oil

during a shock, within 1 week wholesale gasoline prices would rise by 10.2 cents. However, for

every 10 cents per gallon fall in the price of crude oil during a shock, within 1 week wholesale

gasoline prices would fall by only 8 cents per gallon.

• The GAO concluded that during a market price shock, retail gas prices would rise and fall in price

symmetrically, with over half the price adjustment occurring in the first month, but with

complete price adjustment to shocks taking as long as 17 weeks. This result contradicted the

opinions of most of the industry experts they had interviewed, and also contradicted all previous

research except the report by Norman and Shin.

• Like Shin before them, the GAO concluded that their results might have differed from most

previous research because of differences in data, differences in emphasis because they were

primarily interested in market shocks, and some differences in the way they constructed their

econometric model.

Kevin T. Duffy-Deno. The studies discussed above concentrated on gasoline prices at the national

level. Kevin Duffy-Deno attempted to sort out those studies’ mixed results by concentrating on

gasoline prices in Salt Lake City.7 He recognized that local market conditions could make the use of

national averages inappropriate for his analysis. For example, there are 12 suppliers of wholesale

gasoline to the Salt Lake City market, and 5 of them have refineries in the Salt Lake City area. This

availability of gasoline could have a significantly different impact on gasoline pricing compared to

Energy Information Administration/Price Changes in the Gasoline Market 13

7 Duffy-Deno, K. T., “Retail Price Asymmetries in Local Gasoline Markets,” Energy Economics 18 (1996), pp. 81-92.
an area with a more limited number of suppliers, which could also be reflected in different types of

contractual relationships between the different levels of the gasoline distribution chain.

Duffy-Deno built a partitioning model similar to Karrenbrock’s and analyzed the linkage between

weekly wholesale and retail prices in the Salt Lake City area from 1989 through 1993. His price

series included the market shocks from both the Exxon Valdez oil spill in 1989 and the Persian Gulf

War in 1990 and 1991. Average regular unleaded retail gasoline prices in Salt Lake City varied

between 95 cents per gallon and $1.29 per gallon in that time period. He did not also use crude oil

prices in his analysis because of the absence of an appropriate local area data series.

Interestingly, like the GAO before him, he concluded that there was not gasoline price asymmetry at

the retail level during market shocks. But, like most other studies he concluded that there was retail

price asymmetry during normal times. But in Duffy-Deno’s analysis, the complete effect of a price

rise took longer than the complete effect of a price drop.

Duffy-Deno’s study may be summarized as follows:

• It is necessary to examine price asymmetry questions in individual markets, because national

data cannot reflect important regional differences in the gasoline market. For example, there is a

much better match between wholesale and retail prices at the local level than at the national level.

• He built a partitioning model similar to Karrenbrock’s to examine weekly price data from 1989

through 1993. Like the GAO report before him, he found no evidence of retail gasoline price

asymmetry during market shocks. When there was a market shock, complete price adjustment

for both rising and falling prices occurred within 3 weeks of the shock.

• Like most other studies, he found retail gasoline price asymmetry during normal times. But, his

results showed the complete effect of a price rise took longer than the effect of a price drop. On

average, a 10 cents per gallon rise in the wholesale price of gasoline would take 4 weeks to be

reflected as a maximum of 8 cents per gallon increase in the retail gasoline price. On the other

hand, a 10 cents per gallon drop in the wholesale price of gasoline would only take two weeks to

be reflected as a maximum of 4.6 cents per gallon fall in the retail price. Net retail gasoline price

rises in Salt Lake City took longer than price drops, but they went farther.

Severin Borenstein, A. Colin Cameron and Richard Gilbert. Explanation of asymmetric

gasoline price movements requires a careful analysis at many possible market levels. A detailed

analysis of numerous levels of gasoline prices was first undertaken by Borenstein, Cameron and

Gilbert.8 They constructed a special type of partial adjustment model called an error-correction

model to examine weekly or biweekly gasoline prices at the national level from March 1986

through the end of 1992.

They used their model to examine numerous relationships in the gasoline marketing chain. They

searched for asymmetric price movements between the usual crude oil to spot gasoline prices, and

also between crude oil to retail prices, but also spot gasoline to terminal prices, crude oil to terminal

prices, and terminal to retail prices. This enabled them to determine more closely where price

asymmetries may arise.

14 Energy Information Administration/Price Changes in the Gasoline Market

8 Borenstein, S., Cameron, A. C. and Gilbert, R., “Do Gasoline Prices Respond Asymmetrically to Crude Oil Price

Changes?,” Quarterly Journal of Economics (February 1997), pp. 305-339.
They found price asymmetry at the level of crude oil prices to spot gasoline prices, and from

wholesale gasoline price changes to retail price changes. They attributed some of the asymmetry to

the costs of inventory adjustments. Relative shortages of gasoline can be handled by higher prices,

but oversupplies of gasoline must be gradually worked off through the physical distribution system.

Their results may be summarized as follows:

• An error-correction model was applied to weekly or biweekly data from March 1986 to the end of

1992. The results supported the common belief that retail gasoline prices respond more quickly

to increases in crude oil prices than to decreases.

• Pattern asymmetries occur at the crude oil to spot gasoline level, and at the terminal to retail

level. Several hypotheses have been advanced to explain these asymmetries, but none are

completely satisfactory. Gasoline prices may be controlled by a few sellers. Or, there may be

production and inventory lags that affect pricing. Finally, in a time of volatile prices, consumers

may assume higher retail prices they observe at a particular retail station are being reflected

everywhere, so they will not bother to do comparison shopping, making it easier for retail

stations to maintain higher prices.

Nathan S. Balke, Stephen P. A. Brown and Mine K. Yucel. Recently, Balke, Brown and Yucel

(hereafter BCY) revisited the gasoline price asymmetry question.9 They used a modified version of

Borenstein, Cameron and Gilbert’s (hereafter BCG) error-correction model.

To search for asymmetry, they could have also used Bacon’s adjustment model. But, that type of

model contains an implicit behavioral assumption, that retail gasoline prices adjust partially in each

time period toward a target price. That assumption is not critical to use such models to try to detect

price asymmetry. Partitioning models, on the other hand, are primarily statistical models that

attempt to estimate the effect on retail prices of current and lagged wholesale prices or crude oil

prices, without assuming any underlying theory. Since BCY were not interested in explaining

asymmetry, but just determining whether or not it exists, they used a partitioning model.

BCY used weekly data from January 1987 through August 1996. They modified the BCG model to

permit them to search for price asymmetry considering two forms of models, one with price levels

and the other with price changes, the latter they called an “error-correction” model. If there is no

asymmetry, the two types of model are identical. They could not distinguish between the levels

model and the changes model on any theoretical basis.

Unfortunately, their two model types produced opposite results. The levels model showed very

little evidence of price asymmetry, while the error-correction model showed asymmetry is

pervasive. They cannot explain these anomalous results, other than to note that asymmetry tests

can be very model dependent.

The conclusions of this study may be summarized:

Energy Information Administration/Price Changes in the Gasoline Market 15

9 Balke, Norman S., Brown, Stephen P. A. and Yucel, M. K., “Crude Oil and Gasoline Prices: An Asymmetric

Relationship?,” Federal Reserve Bank of Dallas Economic Review (First Quarter 1998), pp. 2-11.
• The authors use weekly price data from January 1987 through 1996. They developed a type of

partitioning model for price changes called an “error correction model,” and they also used a

model for price levels, and tested both models for evidence of asymmetry.

• Their model in price levels indicates that any price asymmetry very rarely occurs, and is small.

However, their model in price changes shows that price asymmetry occurs frequently and is

large. They cannot explain the differences in their results, nor can they provide any economic

interpretation for the differences between their models either theoretically or empirically, nor can

they provide any explanation for why price asymmetry occurs, whether or not it is ever really

present.

Thus, nearly a decade after Bacon did his pathbreaking quantitative work on gasoline price asymmetry

in the United Kingdom, American economists have refined and expanded price asymmetry research.

The American economists have advanced from Bacon’s rather fuzzy conclusions to produce instead

completely contradictory results. The question of whether or not price asymmetry occurs in gasoline

markets is clearly not a closed issue, and is the reason the Energy Information Administration did its

own study of gasoline prices, as described in the next chapter.

16 Energy Information Administration/Price Changes in the Gasoline Market
3. The Energy Information Administration’s

Study of Gasoline Price Changes

In constructing any analysis of price asymmetry, the first consideration is which retail prices should

be studied. As discussed in Chapter 2 in the review of Duffy-Deno’s paper, the local infrastructure

and supply and demand conditions are important. However, a study of multiple cities and/or

several regions is beyond the scope of this report, which is to try to resolve some of the

contradictions in the existing literature as well as to develop a method for price analysis that may

confidently be applied in the future to individual questions when needed. As a result, this report

only analyzes average retail prices in the Midwest.1 The market from crude oil spot prices at

Cushing, Oklahoma, through Gulf Coast refineries and pipelines to the Chicago terminals and from

there throughout the Midwest, provides a consistent series of prices for analysis.2

Understanding retail gasoline price movements requires an understanding of its cost components.

As Figure 3.1 shows, retail gasoline prices can be divided into 3 main components: crude oil prices,

manufacturing and marketing costs and profits, and taxes. Of these components, crude oil prices

are the most important and most volatile and hold the key for the analysis in this report. Taxes

certainly may change, but usually in discrete jumps that are spaced far apart compared to the weekly

price data that are being analyzed here. Similarly, while manufacturing and marketing costs are

certainly not fixed, they change relatively slowly. Crude oil prices, on the other hand, can be

extremely volatile3 even on a daily basis. Thus, for the purpose of analyzing short-term retail

gasoline price movements, it is important to track the prices of crude oil and of gasoline as it moves

through the market structure that was shown in Figure 1.3.

The Energy Information Administration collects much of the data needed for analysis of gasoline

price movements. A first step toward formally searching for price asymmetry in gasoline prices is

simply to look at the data series. Figures 3.2 through 3.6 show prices at various points in the

gasoline distribution chain.

Figure 3.2 shows the price of spot West Texas Intermediate (WTI) crude oil, a popular benchmark

price, at Cushing, Oklahoma, compared to EIA’s average gasoline pump price, including taxes, in

the Midwest. (WTI was chosen because it is widely traded, is an important crude to refineries in the

region, and is used to index the price of many other crude streams.) The similarity of these two

price series is striking, and there is no clear pattern of prices rising faster than they fall on the chart.

Two conclusions are therefore immediately apparent from Figure 3.2. First, crude oil prices are

critical to any analysis of retail gasoline prices. Second, if asymmetry is present, it will require a

sensitive statistical technique to find it.

Energy Information Administration/Price Changes in the Gasoline Market 17

1 The term “Midwest” here equates to Petroleum Administration for Defense (PAD) District 2, which includes the states

of Illinois, Indiana, Iowa, Kansas, Kentucky, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, Oklahoma,

South Dakota, Tennessee and Wisconsin.

2 In addition, a previous EIA report detected an unusually rapid price passthrough in Midwest gasoline prices relative to

other areas of the country, which invited further study. See Assessment of Summer 1997 Motor Gasoline Price Increase,

Energy Information Administration, DOE/EIA-0621 (Washington, DC, May 1998).

3 “Volatility” is an annualized measure of the size of the price changes.
Figure 3.3 shows the relationship between spot crude oil prices and spot gasoline prices at the

refinery. At this level, most previous studies found that the spot gasoline prices moved

symmetrically in response to crude oil price changes. The market is very competitive at this level,

and the existence of a futures market for domestic crude oil permits the knowledge of actual prices

to be disseminated very quickly.

Figures 3.4 and 3.5 show gasoline prices further down the distribution chain. Figure 3.4 shows two

types of spot prices, so it is not surprising that they are closely related. Figure 3.5 shows the close

relationship between pipeline spot prices and rack prices at city terminals in Chicago.

Figure 3.6 shows the relationship between Chicago rack prices and Midwest retail prices. Previous

studies typically found price asymmetry at the retail level.

Finally, is there anything that might be learned from any of EIA’s related models and data series that

could be applied to this study? Figure 3.7 provides an answer. It shows a national average of actual

and forecasted retail gasoline prices. The next week’s changes in the national price are predicted

from a symmetrical response model which consists of a moving average of prior wholesale gasoline

18 Energy Information Administration/Price Changes in the Gasoline Market

$0.453

$0.347

$0.399

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1997

Dollars Per Gallon

Taxes

Mfg/Mkt

Crude Oil

$1.199/Gallon

Figure 3.1 Components Of Retail Regular Gasoline Prices

Crude Oil Prices Are Significant and Variable

Source: Energy Information Administration, Form EIA-878, “Motor Gasoline Price Survey,” and Form EIA-782,

“Monthly Petroleum Product Sales Report.”
Energy Information Administration/Price Changes in the Gasoline Market 19

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

10/5/92

1/5/934/5/937/5/93

10/5/93

1/5/944/5/947/5/94

10/5/94

1/5/954/5/957/5/95

10/5/95

1/5/964/5/967/5/96

10/5/96

1/5/974/5/977/5/97

10/5/97

1/5/984/5/98

Dollars Per Gallon

WTI Spot Midwest (PADD 2 Retail Conventional Regular)

Figure 3.2 Do Midwest Gasoline Retail Prices Move With Crude Oil Prices?

Regular Pump Prices

Source: DRI, Inc. and Energy Information Administration, Form EIA-878, “Motor Gasoline Price Survey.”

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

10/5/92

1/5/934/5/937/5/93

10/5/93

1/5/944/5/947/5/94

10/5/94

1/5/954/5/957/5/95

10/5/95

1/5/964/5/967/5/96

10/5/96

1/5/974/5/977/5/97

10/5/97

1/5/984/5/98

Dollars Per Gallon

WTI Spot US Gulf Spot Conventional Regular

Figure 3.3 Crude Oil Prices and Spot Gasoline Prices Move Together

Source: DRI, Inc.
20 Energy Information Administration/Price Changes in the Gasoline Market

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

10/5/92

1/5/934/5/937/5/93

10/5/93

1/5/944/5/947/5/94

10/5/94

1/5/954/5/957/5/95

10/5/95

1/5/964/5/967/5/96

10/5/96

1/5/974/5/977/5/97

10/5/97

1/5/984/5/98

Dollars Per Gallon

US Gulf Spot Conventional Regular

Chicago Pipeline Conventional Regular

Figure 3.4 Refinery and Pipeline Spot Prices Are Closely Correlated

Source: DRI, Inc.

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

10/5/92

1/5/934/5/937/5/93

10/5/93

1/5/944/5/947/5/94

10/5/94

1/5/954/5/957/5/95

10/5/95

1/5/964/5/967/5/96

10/5/96

1/5/974/5/977/5/97

10/5/97

1/5/984/5/98

Dollars Per Gallon

Chicago Pipeline Conventional Regular

Chicago Rack Conventional Regular

Figure 3.5 Pipeline Spot and Rack Prices

Source: DRI, Inc. and Oil Price Information Service.
Energy Information Administration/Price Changes in the Gasoline Market 21

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

1.10

1.20

1.30

10/5/92

1/5/934/5/937/5/93

10/5/93

1/5/944/5/947/5/94

10/5/94

1/5/954/5/957/5/95

10/5/95

1/5/964/5/967/5/96

10/5/96

1/5/974/5/977/5/97

10/5/97

1/5/984/5/98

Dollars Per Gallon

Chicago Rack Conventional Regular

Midwest (PADD 2 Retail Conventional Regular)

Figure 3.6 Rack Prices and Retail Prices. A Source of Asymmetry?

Source: Oil Price Information Service and Energy Information Administration, Form EIA-878, “Motor Gasoline Price

Survey.”

0

20

40

60

80

100

120

140

4/10/956/10/958/10/95

10/10/9512/10/95

2/10/964/10/966/10/968/10/96

10/10/9612/10/96

2/10/974/10/976/10/978/10/97

10/10/9712/10/97

2/10/984/10/986/10/988/10/98

10/10/9812/10/98

Cents Per Gallon Price Level

-2.0

-1.0

0.0

1.0

2.0

3.0

4.0

5.0

Cents Per Gallon Price Changes

Actual Price Actual Week-to-Week Change Expected Week-to-Week Change

Figure 3.7 Lagged Wholesale Prices Predict Retail Prices at the National Level

Source: Energy Information Administration calculations from Form EIA-878, “Motor Gasoline Price Survey.”
prices. The lower part of Figure 3.7 shows that this simple model provides excellent estimates of

the weekly price changes at the retail level. The lessons of Figure 3.7 are clear: the closeness of the

predicted estimates to the actual values shows that at least for the national level, wholesale and

retail gasoline prices are very closely linked, which helps explain why it is difficult to explain any

asymmetric price movements.

Pattern Asymmetry Tests. EIA began by following the methods of Balke, Brown and Yucel

(BBY), who in turn modified techniques developed by Borenstein, Cameron and Gilbert (BCG).

Their tests were for “pattern asymmetry,” in which prices would rise and/or fall at different rates.

The previous chapter discussed BBY’s paradoxical results. When they estimated their models

using price levels, they got little evidence of price asymmetry. When they estimated their models

using price changes, they got pervasive and strong evidence of asymmetry. In addition to testing for

asymmetry, EIA was aware of this paradox.

Table 3.1 shows the statistical correlations between price levels at the various points in the

distribution chain shown in Figure 1.3. Table 3.2 shows the corresponding correlations for price

changes. Correlation coefficients close to 1.0 generally mean that using those variables together as

explanatory variables in a multivariate regression estimation can give incorrect results. While there

is no precise definition of “close to 1.0,” Table 3.1 clearly shows that all prices (levels) in the

gasoline distribution chain are highly correlated. This is a well known problem in econometrics,

because good explanatory, or “independent,” variables are often related.4

Tables 3.1 and 3.2 show that upstream and downstream prices are highly correlated. This

correlation can cause multicollinearity problems with the BBY (price level) and BCG (price

difference) models because they utilize both upstream and downstream prices as explanatory

variables. Another potential problem is that the time frame for their analyses includes the Gulf War

period when crude oil and petroleum product markets were extremely volatile, which could affect

the stability of their models. Thus, this report limited itself to the post-Gulf War period, using

22 Energy Information Administration/Price Changes in the Gasoline Market

Midwest

Pump Prices

Chicago Rack

Prices

Chicago

Pipeline

Prices

Gulf Coast

Spot Prices

Crude Oil

Spot Prices

Midwest Pump Prices 1.0 .88 .85 .81 .74

Chicago Rack Prices 1.0 .99 .95 .83

Chicago Pipeline Prices 1.0 .96 .82

Spot Gulf Coast Prices 1.0 .86

Crude Oil Spot Prices 1.0

Table 3.1 Gasoline Price Levels Move Together, Creating Statistical Headaches

Note: Correlation coefficient values of 1.0 means the variables move in exactly the same way. A value of zero means the

price series move independently. Weekly data from October 1992 through June 1998.

Source: Correlation coefficients from Energy Information Administration calculations.

4 In an econometric model highly correlated explanatory variables may violate the standard modeling assumption of

independence, because they possess approximately the same information that will cause the model to produce unreliable

estimates. This very common situation is called “multicollinearity.” Highly correlated explanatory variables need to be

checked for potential multicollinearity problems.
weekly price data from October 1992 through June 1998 to test for pattern and amount asymmetry

between Midwest downstream (e.g. wholesale and retail gasoline) prices resulting from upstream

(e.g. crude oil and spot gasoline) price changes. Also, having found that multicollinearity was a

problem during estimation and because the pattern asymmetry test results depended on lag length,

this study used a modified version of the BBY model, where lags of the dependent variable were

eliminated from the specification. (See Appendix C, EQN C.2 for the estimated equation and

Appendix D for the mathematical derivation.) The results of the pattern asymmetry testing are

shown in Table 3.3.

Table 3.3 shows that there is no evidence of pattern asymmetry between crude oil price changes and

gasoline price changes at all levels (far right column). However, that result is complicated by the

fact that there is much more seasonality in gasoline than there is in crude oil. There was strong

evidence of pattern asymmetry between wholesale and retail gasoline prices (bottom row).

Midwest retail prices rise faster than they fall with respect to changes in Gulf Coast gasoline spot

prices, Chicago pipeline prices, and Chicago rack prices.

Price Passthrough Analysis. An equally important question to whether or not there is “pattern

asymmetry” is whether or not there is “amount asymmetry,” i.e., whether changes in crude oil

prices or wholesale gasoline prices are ultimately reflected fully in retail gasoline prices. For these

tests EIA used the passthrough models developed by Zyren.5 The passthrough models for this

analysis are described in Appendix B. Passthrough models were developed in order to determine

the amount of time it takes for upstream cost changes to be reflected in downstream price changes.

Price passthrough models use (weekly) lags of upstream prices to account for the length of time

required for downstream markets to adjust to upstream price changes. Table 3.4 shows the results

of a passthrough model analysis from upstream prices to Midwest retail prices.

Energy Information Administration/Price Changes in the Gasoline Market 23

Midwest

Pump Prices

Chicago Rack

Prices

Chicago

Pipeline

Prices

Gulf Coast

Spot Prices

Crude Oil

Spot Prices

Midwest Pump Prices 1.0 .21 -.02 -.07 -.06

Chicago Rack Prices 1.0 .80 .74 .49

Chicago Pipeline Prices 1.0 .82 .59

Spot Gulf Coast Prices 1.0 .59

Crude Oil Spot Prices 1.0

Table 3.2 Gasoline Price Changes Have Less Concurrent Correlation

Than Levels

Note: Correlation coefficients with values of 1.0 means the variables change in exactly the same way. A value of -1.0

means that a rise in one price change is always accompanied by an identical fall in the other price change. A value close to

zero indicates the price changes move independently in the same time period. Weekly data from October 1992 through June

1998.

Source: Correlation coefficients from Energy Information Administration calculations.

5 See, for example, Motor Gasoline Assessment: Spring 1997, Energy Information Administration, DOE/EIA-0613

(Washington, DC, July 1997), and Assessment of Summer 1997 Motor Gasoline Price Increase, Energy Information

Administration, DOE/EIA-0621 (Washington, DC, May 1998).
The results shown in Table 3.4 demonstrate that about 60 percent of upstream price changes are

fully reflected in Midwest retail gasoline price changes in about two weeks, about 75 percent of the

changes are reflected after 4 weeks, and the price passthrough is completed in 8 or 9 weeks.6 The

fact that the estimated coefficients show a steady, cumulative price effect over time, and that the

price passthrough is eventually fully reflected at retail, makes market sense. Also, rack prices,

which are the closest to retail, have the most rapid passthrough (54 percent, versus 35 percent for

the other upstream prices), which is also a reasonable result. Passthrough results are discussed in

more detail in Appendix B.

Table 3.5 shows price passthrough models from the important spot crude oil level to wholesale

levels. Changes in crude oil prices are fully reflected at Gulf Coast gasoline prices, Chicago

pipeline prices, and Chicago rack prices in about 4 weeks. Again, the results of the model are

consistent and show eventual complete price passthrough, or amount symmetry, which provides

some evidence of model validity.

Table 3.6 shows passthrough models at the wholesale level, an illustration of the robustness of the

model formulation. Prices between Gulf Coast gasoline prices and Chicago pipeline prices,

between Chicago pipeline prices and Chicago rack prices, and between Gulf Coast spot prices to

Chicago rack prices all have complete passthrough in from 1 to 5 weeks. The complete price

passthrough, in shorter times than passthrough to retail because of shorter distances, again provides

evidence for the reasonableness of the price passthrough models.

The results of statistical testing for amount asymmetry are shown in Table 3.7. The values shown in

the table are downstream (dependent variable) cumulative percentages for the passthrough of

upstream (independent variable) price changes. A value of 100.0 indicates exact 100 percent

passthrough (after an appropriate number of weeks) of upstream price changes to the downstream

24 Energy Information Administration/Price Changes in the Gasoline Market

Chicago Rack

Prices

Chicago Pipeline

Prices

Gulf Coast Spot

Prices

Crude Oil Spot

Prices

Gulf Coast Spot Prices 0.852

(0.531)

Chicago Pipeline Prices 2.615**

(0.036)

1.128

(0.344)

Chicago Rack Prices 1.359

(0.259)

0.988

(0.433)

1.533

(0.193)

Midwest Retail Prices 5.407***

(0.000)

2.346***

(0.019)

3.150***

(0.003)

0.721

(0.673)

Table 3.3 Statistical Tests Show “Pattern Asymmetry”

of PADD 2 Price Changes at the Retail Level

Notes: Dependent variables are on the left. Numbers in the table are Wald Test F-statistic for pattern asymmetry; p-values

appear in parenthesis below test statistic. Tests are on difference form of pattern asymmetry equation.

** = statistically significant at the .05 level.

*** = statistically significant at the .01 level.

Source: Appendix tables C.1-C.3.

6 These results are consistent with previous work. See Motor Gasoline Assessment: Spring 1997, Energy Information

Administration, DOE/EIA-0613 (Washington, DC, July 1997).
price. The values in the table show that amount symmetry is prevalent throughout most of the

gasoline distribution system. There are only two out of ten cases which indicate the presence of

significant amount asymmetry: there appears to be less than 100 percent passthrough of rack prices

to retail, and greater than 100 percent passthrough of spot prices to rack prices.

In summary, these results consistently show that there is little evidence of amount asymmetry but

substantial evidence of pattern price asymmetry in the Midwest retail motor gasoline markets.

However, even the pattern asymmetry may be a statistical artifact, as can be seen in Figure 3.8. In

the summer of 1995, the spot price appears to be falling faster than the retail price, but a lagged

market adjustment would make this apparent pattern asymmetry disappear. Similarly, in the fall of

1996, spot prices dropped sharply while retail prices continued to rise, but lagged price passthrough

to the retail markets shows this pattern asymmetry may only have been an illusion. This possibility

is further considered in Appendix A (Figure A.5). In such cases, however, since after all lagged

price adjustments have been completed the wholesale price increases will be almost completely

reflected in retail prices, there is little evidence of “amount asymmetry,” i.e., the consumer in the

Midwest normally will ultimately have lower spot market costs reflected at retail.

Energy Information Administration/Price Changes in the Gasoline Market 25

Lagged Weeks Chicago Rack

Prices

Chicago Pipeline

Prices

Gulf Coast Spot

Prices

Crude Oil Spot

Prices

1 53.6% 34.4% 36.0% 36.0%

2 60.7% 60.2% 60.8% 57.0%

4 75.7% 78.0% 79.2% 77.6%

6 80.3% 87.0% 92.6% 91.9%

8 92.6% 97.5% 100.7% 103.7%

Total 92.6% 97.5% 100.7% 110.6%

Table 3.4 Price Passthrough Analysis to Midwest Retail Prices

Notes. Numbers in table are cumulative percentages. Crude oil total is for 9-week lags.

Source: Energy Information Administration calculations based on Appendix Table B.1.

Lagged Weeks WTI To Midwest

Retail Prices

WTI To Chicago

Rack Prices

WTI To Chicago

Pipeline Prices

WTI To Gulf Coast

Spot Prices

0 59.3% 79.1% 78.8%

1 36.0% 89.7% 79.2% 85.7%

2 57.0% 86.8% 84.3% 98.1%

4 77.6% 106.1% 113.8% 106.0%

6 91.9%

8 103.7%

Total 110.6% 106.1% 113.8% 106.0%

Table 3.5 Price Passthrough from West Texas Intermediate Crude Oil

Note: Numbers in table are cumulative percentages.

Source: Energy Information Administration calculations based on Appendix Table B.2.
Whether or not all these results would also hold up in other regions of the country is an open

question. Drawing conclusions about other regions would require a separate consideration of the

market infrastructure and a choice of appropriate data series to perform the statistical tests.

Conclusions. This report has analyzed weekly gasoline price changes in the Midwest, from

October 1992 through June 1998, and has reached the following conclusions.

• Wholesale gasoline price rises and price drops are basically symmetric with respect to changes in

crude oil prices.

• Retail gasoline prices in the Midwest can rise much faster than they fall in response to wholesale

gasoline price changes. This is called pattern asymmetry. Also, after all lagged price

26 Energy Information Administration/Price Changes in the Gasoline Market

Lagged

Weeks

Gulf Coast Spot To Chicago

Rack

Chicago Pipeline To

Chicago Rack

Gulf Coast Spot To Chicago

Pipeline

Price

Changes

Price Levels Price

Changes

Price Levels Price

Changes

Price Levels

0 65.2% 63.8% 62.9% 63.2% 83.0% 88.4%

1 101.9% 100.3% 102.2% 102.3% 88.1% 87.3%

2 101.3% 99.4% 91.8% 90.0%

3 108.2% 103.2% 99.1% 98.0%

4 108.0% 102.7%

5 117.2% 111.8%

Table 3.6 Wholesale Price Passthrough. Levels and Differences Produce

Stable Results

Note: Numbers in table are cumulative percentages.

Source: Energy Information Administration calculations based on Appendix Table B.3.

Chicago Rack

Prices

Chicago Pipeline

Prices

Gulf Coast Spot

Prices

Crude Oil Spot

Prices

Gulf Coast Spot

Prices

106.0%

(0.626)

Chicago Pipeline

Prices

99.1%

(0.859)

113.8%

(0.281)

Chicago Rack

Prices

102.2%*

(0.085)

117.2%***

(0.002)

106.1%

(0.636)

Midwest Retail

Prices

92.6%**

(0.027)

97.5%

(0.432)

101.0%

(0.905)

110.6%

(0.506)

Table 3.7 Statistical Tests Show Widespread “Amount Symmetry” of Price

Changes

Notes: Dependent variables are on the left. Numbers in the table are cumulative passthrough percentages of independent

variable price changes passed through to the dependent variable. The p-values for the Wald test F-Statistic of the cumulative

percentage value being equal to 100.0 are shown in parentheses. Tests are on difference form of passthrough equations for

weekly data from October 1992 through June 1998. * = statistically significant at the .10 level. ** = statistically significant

at the .05 level. *** = statistically significant at the .01 level.

Source: Energy Information Administration calculation based on appendix Tables B.1 - B.3.
adjustments have been completed the wholesale price increase or decrease will almost

completely pass through to the retail level, so there is little evidence of amount asymmetry.

• The very rapid price passthrough from upstream to downstream wholesale markets indicates that

future studies should use daily instead of weekly prices.

• The conclusions of this type of analysis depend importantly on various characteristics, including

frequency and location specificity, of the data used in the models. Thus, conclusions about price

asymmetry at the city or state level would necessitate a collection of data and an examination of

the structure of the local gasoline market on a case-by-case basis, so debates about the overall

dynamics of gasoline prices will probably continue indefinitely until all markets have been

investigated.

Energy Information Administration/Price Changes in the Gasoline Market 27

30

35

40

45

50

55

60

65

70

75

1/2/953/2/955/2/957/2/959/2/95

11/2/95

1/2/963/2/965/2/967/2/969/2/96

11/2/96

1/2/973/2/975/2/977/2/979/2/97

11/2/97

1/2/983/2/985/2/987/2/989/2/98

11/2/98

Spot Cents Per Gallon

85

90

95

100

105

110

115

120

125

130

Retail Cents Per Gallon

Gulf Coast Spot Conventional Regular (weekly)

Midwest Retail Conventional Regular (weekly)

Figure 3.8 Lags Between Spot and Retail Prices Can Create Pattern Asymmetry

Source: DRI, Inc. and Energy Information Administration, Form EIA-878, “Motor Gasoline Price Survey.”
Appendix A

A Moving Average / Symmetry Primer

The idea behind symmetric or asymmetric downstream price responses caused by upstream price

changes can be illustrated with a simulation using a moving representation of the price response:

y xxxx t =+ + + + + tttt −−−− 5 0 35 0 25 0 10 0 15 0 10 1 234 . * .* .* .* .*x x t t − − 5 6 + 0 05 . * EQN A.1

where yt is the downstream price at time t.

xt-i are the lagged upstream price changes.

The coefficients were chosen to have declining weights and to sum exactly to 1.0, thus yt is modeled

by a weighted sum of the lagged upstream price changes. The weighting coefficients in EQN A.1

are very similar to the regression estimated coefficients for the U.S. Gulf Coast spot motor gasoline

price passthrough to PADD 2 retail prices (see column 3 in Table B.1).

The first application for this model is to demonstrate the concept of AMOUNT symmetry, which is

illustrated in Figure A.1. The “simulated” upstream price was constructed to be a simple step

function, beginning at a constant value of 50 cents per gallon, uniformly increasing by 2.5 cents for

each of two periods (#101 and #102) and then remaining constant at 55 cents thereafter. The

resulting downstream price behavior shown in the figure shows a lagged one period response after

the initial downstream price increase, and shows a complete response 6 periods (#108) after the

downstream price movement, after which the value is constant at 60 cents. The property of amount

symmetry is preserved because the 5 cent total upstream price increase eventually results in the

downstream price increasing by 5 cents per gallon, given a sufficiently long time period for the lag

response. The concept of amount symmetry was incorporated into the model (EQN A.1) by having

the coefficients of the lagged independent variables sum to 1.0.

The second application of this model is to demonstrate the concept of PATTERN symmetry, which

simply means that the response pattern of downstream price to a positive or a negative change in the

upstream price will be identical, although opposite in direction. Figure A.2 illustrates the case of

pattern symmetry. Note that the downstream response pattern to an upstream price increase and

decrease can be seen to be equal and opposite. The upstream price changes used are similar to those

used in the previous example, i.e., a 2.5 cent increase/decrease for two consecutive time periods for

an overall 5 cent change. The positive price changes shown in Figure A.1 are replicated in Figure

A.2, along with the upstream and downstream paths for a price decrease. Close examination of the

downstream price shows that the response to an upstream price decrease is equal and opposite to

that for the upstream price increase. This illustrates the concept of a downstream PATTERN

symmetry price response.

Furthermore, a third application of this model is to demonstrate that pattern asymmetry can be an

artifact of time lags in the system, and that this occurs when there is a temporary peaking of the

Energy Information Administration/Price Changes in the Gasoline Market 29
30 Energy Information Administration/Price Changes in the Gasoline Market

48

50

52

54

56

58

60

62

9092949698

100102104106108110112114116118120

Observaton Number

Cents Per Gallon

Upstream Price Downstream Price

Figure A.1 Example of Amount Symmetry

Source: Energy Information Administration simulation.

44

46

48

50

52

54

56

58

60

62

9092949698

100102104

106

108110112114116118120

Observation Number

Cents Per Gallon

Upstream Price Increase Downstream Price Increase

Upstream Price Decrease Downstream Price Decrease

Figure A.2 Example of Pattern Symmetry

Source: Energy Information Administration simulation.
upstream price. The specification of this downstream price model assumes a declining lagged

response, with 60 percent of the passthrough during the first two time periods and complete

passthrough after 6 weeks (EQN A.1). The upstream price was chosen to provide a symmetrical 5

cent price increase and decrease spread over 10 time periods: the upstream price was assumed

constant at 50 cents until period #100 when the price increased by 1 cent per period for 5 periods

(i.e. 5 cent rise), and then declined at the rate of 1 cent per period until the old equilibrium level was

reached after 5 periods (#106 to #110). This peak was followed by a symmetrical price decrease of

the same magnitude. These symmetrical upstream price changes are shown in Figure A.3, along

with the simulated downstream price passthrough. The response on the downstream pricing, which

is seen to be identical regardless of the direction of the initial movement, shows a number of

asymmetric properties:

• The downstream price responds with a 1 period lag to the initial upstream price change.

• The downstream price requires 6 periods to reach its maximum (minimum), whereas the

upstream price takes only 5 periods.

• The downstream price peaks 2 periods after the upstream price maximum (minimum).

• The downstream price takes longer to return to equilibrium, requiring a full 9 time periods after

the price maximum (minimum), whereas the upstream price took only 5 periods to return to the

original level.

Energy Information Administration/Price Changes in the Gasoline Market 31

40

42

44

46

48

50

52

54

56

58

60

62

90 94 98 102 106 110 114 118 122 126 130 134 138

Observation Number

Cents Per Gallon

Upstream Price Downstream Price

Figure A.3 Example of Apparent Pattern Asymmetry Caused

by a Lagged Response

Source: Energy Information Administration simulation.
• The downstream price has a peak value of approximately 59 cents for the price increase (or 51

cents for the price decrease), nearly 4 cents greater (smaller) than the equilibrium value, whereas

the upstream price peaked (bottomed) at the full 5 cent above (below) equilibrium. This occurs

even though the model is amount symmetric.

• The downstream price peak has an asymmetric (skewed to the right) shape, with an up slope

steeper than the down slope (or downslope steeper than the upslope for the price decrease) and

with a larger area under the curve to the right of the peak maximum (minimum) than to the left,

whereas the upstream price curve is symmetric. This is an example of PATTERN

ASYMMETRY.

In conclusion, this simulation shows that by using a symmetric upstream price spike and symmetric

market responses to upstream price increases and decreases, THAT IT IS POSSIBLE TO

EMPIRICALLY INDUCE PATTERN ASYMMETRY IN THE DOWNSTREAM PRICE

RESPONSE.

The finding of an asymmetrical price response at the retail market, whatever its possible cause, does

not seem to have great significance. This is demonstrated in Figure A.4, which compares the actual

Midwest retail prices with those from the regression fitted model. The fitting equation used was the

USG spot to retail price passthrough equation in Appendix B (Table B.1) which assumes a

symmetrical market response. The extremely high correlation between an asymmetric price series

and a fitted symmetric model indicates that even if there is some evidence that there is price

32 Energy Information Administration/Price Changes in the Gasoline Market

90

100

110

120

130

10/19/92

2/19/936/19/93

10/19/93

2/19/946/19/94

10/19/94

2/19/956/19/95

10/19/95

2/19/966/19/96

10/19/96

2/19/976/19/97

10/19/97

2/19/986/19/98

Cents Per Gallon

Actual Forecast

Figure A.4 Actual and Forecasted Midwest Retail Gasoline Prices

Source: Energy Information Administration Form EIA-878, “Motor Gasoline Price Survey.”
asymmetry between the refinery spot price and the PADD 2 retail price, the amount actually due to

an asymmetric market response rather than a symmetric lagged response is not large.

A further illustration of the response of (downstream) motor gasoline retail prices to changes in

USG spot prices is illustrated in the next figure, which expands Figure A.4 for the 01JAN96 to

30JUN97 time period and also includes the spot price; both the retail and fitted retail price series are

identical to those in Figure A.4. Figure A.5 clearly shows for a number of time periods that the

actual retail price is still rising when spot prices fall after reaching a peak, and also that actual

(downstream) retail prices show a slower downward response than the “corresponding” (upstream)

spot price declines. The crucial point of Figure A.5 is that a symmetrical lag regression model

explains this seemingly slow downstream price response to upstream price declines. The

SYMMETRICAL price passthrough model in Table B.1 provides a close fit for both upward AND

downward retail price response to upstream price movements. Figure A.5 provides strong visual

evidence that even if pattern asymmetry actually exists in downstream price movements, that the

overall effects on the market are minimal. Actual quantification of this effect will be left for the

next study.

Energy Information Administration/Price Changes in the Gasoline Market 33

40

50

60

70

80

12/31/95

1/31/962/29/963/31/964/30/965/31/966/30/967/31/968/31/969/30/96

10/31/9611/30/9612/31/96

1/31/972/28/973/31/974/30/975/31/97

Spot Cents Per Gallon

90

100

110

120

130

Retail Cents Per Gallon

Gulf Coast Spot Actual Retail

Fitted Retail

Figure A.5 Gulf Coast Spot and Midwest Retail Gasoline Prices

Model of Symmetrical Retail Market Response

Source: Energy Information Administration Form EIA-878, “Motor Gasoline Price Survey.”
Appendix B

Quantitative Explanation of Motor Gasoline

Market Price Passthrough

As part of the asymmetry study, an analysis was undertaken using industry and EIA data to explore

the speed of price change passthroughs from the various upstream spot markets to the retail market

for individual regions of the U.S. The results reported below update the results published in the

previous report1 and will be used to form the basis for further research on pricing mechanisms in

petroleum product markets.

Estimates showed that the price passthrough from various spot markets to the retail level is

complete by two months with price passthrough to the various spot markets taking substantially

less time. The unusually rapid Gulf Coast spot to retail price passthrough speed for PADD 2 (80

percent within 4 weeks vs. 50 percent in the other PADD’s) found in the previous study was verified

and is consistent with the other results of this study.

An attempt was made to estimate the speed of adjustment of motor gasoline retail prices as a

function of lagged downstream spot prices by using weekly EIA data from the Form EIA-878,

“Motor Gasoline Price Survey.” The gasoline price used was that of PADD 2 conventional regular

gasoline (RETPADD2). Weekly averages of West Texas intermediate crude oil (WTISPOT) spot

price, the U.S. Gulf Coast (USGSPOT) conventional motor gasoline, Chicago pipeline (CHIPIPE)

and Chicago rack (CHIRACK) conventional regular prices were calculated from daily values. The

daily WTISPOT, USGSPOT, and CHIPIPE prices were obtained from DRI, Inc. and the

CHIRACK prices were obtained from Oil Price Information Service. Investigation of the time

series properties of the price data was performed in order to assist in specifying the appropriate

form of the model; for example, data with unit root properties are best analyzed using first

differences, whereas stationary series can be estimated in level form. Phillips-Perron and

Augmented Dickey-Fuller tests could not reject the hypothesis that the retail gasoline and averaged

WTI spot data had a unit root, whereas the averaged spot gasoline data rejected the unit root

hypothesis (USGSPOT at the 10 percent level, CHIPIPE and CHIRACK at the 5 percent level but

not at the 1 percent level). Since all series had a strong autoregressive component, first differences

were used for the regression analysis. The retail prices and the weekly averages of the spot prices

were defined to correspond to the same week, so that the retail prices were estimated as a function

of lagged spot prices.

The interpretation of the regression model depends on the form of the lag parameters in the model.

For example, the estimated model has the form:

∆∆ ∆ ∆ y b x b x b x cz e t = + ++ + + 1 12 2 t t k tk −− − L ot t

Energy Information Administration/Price Changes in the Gasoline Market 35

1 Energy Information Administration, Assessment of Summer 1997 Motor Gasoline Price Increase, DOE/EIA-0621

(Washington, DC, May 1998), Appendix E, p. 81.
or, more succinctly

∆ ∆ y b x cz e t i

i

k

= ++ t i ot t

=

∑ −

1

This model can be interpreted as follows. The lagged form shown in this model means that a

change (a)in the independent variable (X) will affect the change in the dependent variable (Y) for a

number of time periods. In particular, a change in X during the time period (t-k) not only affects the

change in Y during the period (t-k), but also for a number of time periods in the future (i.e., (t-k+1),

(t-k+2), and all the way to (t)). More formally, aXt-k affects not only the change in aYt-k, but also in

aYt-k+1, aYt-k+2, ..., aYt. The other parameter Zt-k has an immediate effect on the change in aYt-k

and a permanent change in the level of Yt-k during time period (t-k).

The equation actually estimated for this study is

∆ ∆ DOWN UP D OCT AR MA Terms e t i

i

k

= ++ + t i t

=

∑ β −

1

04 93 , EQN B.1

Where:

∆ is the week to week change

DOWNt is the downstream (relative to the upstream) motor gasoline price (in cents

per gallon) for week t

UPt is the upstream gasoline or crude oil spot price (in cents per gallon) for week t

D04OCT93 is the dummy variable for the retail Federal tax increase in October, 1993

et is the random error term at time t

AR, MA are autoregressive and moving average terms

Amount asymmetry in the passthrough models can be checked by determining whether the sum of

the current and lagged upstream variables sum to one. The statistical test for amount asymmetry is

to test the hypothesis:

∑ βi = 10. EQN B.2

where the sum is taken over all k upstream price lags. It should be noted that large p-values mean

that the null hypothesis of amount symmetry CANNOT be rejected. Tables B.1 - B.3 show the

parameter estimates for the various market levels. The estimation method used was Ordinary Least

Squares (OLS). The AR, MA terms are used to make the regression residuals test as random. The

lag length was chosen by using the one which minimized the Akaike Information Criterion (AIC)

36 Energy Information Administration/Price Changes in the Gasoline Market
value; this also provided parameter estimates for the spot price which did not increase when another

lag was added to the equation. A priori, one would expect to see an approximately 1:1 eventual

pass-through of spot price changes in a perfectly competitive market and would also expect the

influence of a spot price change to decrease monotonically over time after the first or second time

period. The estimation results reported in the table are reasonable and are very similar to those

appearing in the previous study (see footnote 1). These results show, depending on the market

level, that anywhere between 93 to 117 percent of a spot price change is passed through to retail

within two months and that the lag effect does tend to decrease over time.

When interpreting the results shown in the tables (especially Table B.2) it is important to remember

the definition of the lagged variables: the retail price is a Monday morning open-of-business price,

whereas the other variables are defined as the weekly average price for the current week. Thus

current downstream prices other than retail would be explained by current and lagged upstream

prices, whereas the current retail price is only explained by lagged upstream prices.

The coefficient estimates shown in Table B.3 indicate a rapid price passthrough; this is especially

true for the USGSPOT to CHIPIPE analysis, where about 85 percent a change in the upstream spot

price is passed through during the same week. This very rapid passthrough and the complex ARMA

structure required to make the residuals pass as white noise indicate that the weekly time frequency

utilized for this spot market analysis is too long, and that further analysis should be performed using

daily prices.

Energy Information Administration/Price Changes in the Gasoline Market 37
38 Energy Information Administration/Price Changes in the Gasoline Market

Independent Variable CHIRACK CHIPIPE USGSPOT WTISPOT

Change in Downstream Price (dependent variable):

PADD 2 Retail Price, conventional regular gasoline

Parameter

CONSTANT 0.011 0.009 0.018 0.875**

(0.028) (0.023) (0.055) (0.436)

∆UP(t-1) 0.536*** 0.344*** 0.360*** 0.360***

(0.023) (0.021) (0.026) (0.043)

∆UP(t-2) 0.071*** 0.258*** 0.248*** 0.210***

(0.027) (0.025) (0.027) (0.043)

∆UP(t-3) 0.067** 0.070*** 0.069** 0.078*

(0.027) (0.025) (0.027) (0.044)

∆UP(t-4) 0.083*** 0.108*** 0.114*** 0.128***

(0.027) (0.025) (0.027) (0.046)

∆UP(t-5) 0.055** 0.024 0.064** 0.043

(0.027) (0.025) (0.027) (0.046)

∆UP(t-6) -0.009 0.066*** 0.070*** 0.100**

(0.027) (0.025) (0.026) (0.046)

∆UP(t-7) 0.123*** 0.032 0.081*** 0.009

(0.023) (0.025) (0.026) (0.045)

∆UP(t-8) 0.072*** 0.108**

(0.021) (0.045)

∆UP(t-9) 0.069

(0.045)

D04OCT93 4.759*** 4.893*** 4.800*** 5.428***

(0.700) (0.721) (0.889) (1.234)

WEEKLY DUMMY NO NO NO YES***

(1.680)

AR(1) 0.192 -0.920***

(0.125) (0.034)

MA(1) -0.346*** -0.603** 0.825***

(0.055) (0.101) (0.054)

AR(2) 0.080

(0.065)

MA(3) 0.159*** 0.170***

(0.048) (0.065)

AMOUNT ASYMMETRY F-STATISTIC 4.923** 0.619 0.014 0.445

p-value (0.027) (0.432) (0.905) (0.506)

Sum of Spot Lags 0.926 0.975 1.007 1.106

AIC 2.268181 2.437354 2.687731 3.226603

Adj. R^2 0.734 0.688 0.599 0.409

F-Statistic 92.2 60.5 41.3 4.25

D.W. Statistic 1.99 2.00 2.08 2.00

Table B.1 OLS Regression Results for Retail Price Passthrough

Weekly Prices from October 5, 1992 to June 29, 1998
Energy Information Administration/Price Changes in the Gasoline Market 39

Table B.1 Continued

Notes:

The general form of the linear model is shown in Equation B.1.

All prices are in cents per gallon.

“∆" is the weekly price change.

The (t-1), (t-2), ..., (t-9) refer to lagged values.

UP refers to the weekly average price for regular, conventional motor gasoline at U.S. Gulf Coast (USGSPOT);

Chicago Pipeline price (CHIPIPE); Chicago Rack price (CHIRACK); or it refers to the weekly

average spot price for West Texas Intermediate Crude Oil (WTISPOT)

D04OCT93 is a dummy variable corresponding to the change in Federal tax on motor gasoline.

WEEKLY DUMMY refers seasonal variables, the joint test F-statistic is reported in parentheses.

AMOUNT ASYMMETRY F-STATISTIC refers to the joint hypothesis test:

∆UP(t-1)+∆UP(t-2)+ ... +∆UP(t-k) = 1.0.

A p-value < 0.05 indicates rejection of the joint hypothesis.

Standard errors appear in parentheses below the parameter estimates.

*** indicates significant at 1% criteria (p-value < 0.01)

** indicates significant at 5% criteria (p-value < 0.05)

* indicates significant at 10% criteria (p-value < 0.10)
40 Energy Information Administration/Price Changes in the Gasoline Market

Independent Variable WTISPOT WTISPOT WTISPOT WTISPOT

Change in Downstream Price (dependent variable):

Parameter ∆USGSPOT

conv/reg

∆CHIPIPE

conv/reg

∆CHIRACK

conv/reg

∆RETPADD 2

conv/reg

CONSTANT 0.633 0.149 1.311** 0.875**

(0.599) (0.621) (0.621) (0.436)

∆WTI(t) 0.788*** 0.791*** 0.593***

(0.060) (0.055) (0.052)

∆WTI(t-1) 0.070 0.002 0.304*** 0.360***

(0.059) (0.056) (0.055) (0.043)

∆WTI(t-2) 0.123** 0.051 -0.029 0.210***

(0.060) (0.056) (0.055) (0.043)

∆WTI(t-3) 0.197*** 0.295*** 0.193*** 0.078*

(0.061) (0.056) (0.054) (0.044)

∆WTI(t-4) -0.117* 0.128***

(0.060) (0.046)

∆WTI(t-5) 0.043

(0.046)

∆WTI(t-6) 0.100**

(0.046)

∆WTI(t-7) 0.009

(0.045)

∆WTI(t-8) 0.108**

(0.045)

∆WTI(t-9) 0.069

(0.045)

D04OCT93 5.428***

(1.234)

WEEKLY DUMMY YES*** YES*** YES*** YES***

(1.688) (2.668) (1.867) (1.680)

AR(1) 0.218***

(0.060)

MA(1) 0.129**

(0.065)

AR(2) -0.135** 0.080

(0.063) (0.065)

AR(6) -0.151** -0.165*** -0.307***

(0.066) (0.062) (0.062)

OTHER ARMA TERMS AR(7,14) AR(4,7) AR(3,13) MA(3)

AMOUNT ASYMMETRY F-STATISTIC 0.238 1.167 0.225 0.445

p-value (0.626) (0.281) (0.636) (0.506)

Sum of Spot Lags 1.06 1.138 1.061 1.106

AIC 3.855444 3.839309 3.768162 3.226603

Adj. R^2 0.484 0.568 0.494 0.409

F-Statistic 5.64 7.62 5.90 4.25

D.W. Statistic 1.99 2.01 2.08 2.00

Table B.2 OLS Regression Results for Price Passthrough from WTI

Weekly Prices from October 5, 1992 to June 29, 1998
Energy Information Administration/Price Changes in the Gasoline Market 41

Table B.2 (Continued)

Notes:

The general form of the linear model is shown in Equation B.1.

All prices are in cents per gallon.

"∆" is the weekly price change.

The (t), (t-1), (t-2), ..., (t-9) refer to current and lagged values.

WTI refers to the weekly average price for West Texas Intermediate crude oil.

D04OCT93 is a dummy variable corresponding to the change in Federal tax on motor gasoline.

WEEKLY DUMMY refers seasonal variables, the joint F-statistic is reported in parentheses.

AMOUNT ASYMMETRY F-STATISTIC refers to the joint hypothesis test:

∆WTI(t)+∆WTI(t-1)+ ... +∆WTI(t-k) = 1.0.

A p-value < 0.05 indicates rejection of the joint hypothesis.

Standard errors appear in parentheses below the parameter estimates.

*** indicates significant at 1% criteria (p-value < 0.01)

** indicates significant at 5% criteria (p-value < 0.05)

* indicates significant at 10% criteria (p-value < 0.10)
42 Energy Information Administration/Price Changes in the Gasoline Market

Independent

Variable

∆CHIPIPE ∆USGSPOT ∆USGSPOT CHIPIPE USGSPOT USGSPOT

Downstream Price (dependent variable):

←--------------------PRICE CHANGE-----------------→

Downstream Price (dependent variable)

←----------------PRICE LEVEL---------------→

∆CHIRACK

con/reg

∆CHIRACK

con/reg

∆CHIPIPE

con/reg

CHIRACK

con/reg

CHIRACK

con/reg

CHIPIPE

con/reg

CONSTANT -0.001 0.012 -0.630 1.155 -1.584 2.691

(0.010) (0.049) (0.472) (0.710) (2.701) (2.269)

UP(t) 0.629*** 0.652*** 0.830*** 0.632*** 0.638*** 0.844***

(0.018) (0.029) (0.036) (0.018) (0.030) (0.036)

UP(t-1) 0.393*** 0.367*** 0.051 0.391*** 0.366*** 0.030

(0.018) (0.029) (0.037) (0.018) (0.029) (0.037)

UP(t-2) -0.007 0.036 -0.010 0.027

(0.029) (0.037) (0.029) (0.037)

UP(t-3) 0.069** 0.073** 0.038 0.080**

(0.029) (0.037) (0.029) (0.036)

UP(t-4) -0.002 -0.005

(0.030) (0.029)

UP(t-5) 0.092*** 0.091***

(0.030) (0.030)

WEEKLY DUMMY NO NO YES** NO NO YES*

(1.587) (1.351)

AR(1) -0.113* 0.656*** .956*** 0.684***

(0.063) (0.091) (0.044) (0.062)

MA(1) -0.652*** -0.305*** 0.140*

(0.047) (0.116) (0.081)

AR(3) -0.226*** -0.228*** -0.123***

(0.061) (0.057) (0.043)

AR(6) -0.179*** -0.196*** -0.203***

(0.059) (0.057) (0.063)

Other ARMA terms AR(12) AR(2,4,5,7,9) AR(5,9) MA(12) AR(11,14),

MA(3)

AMOUNT

ASYMMETRY

F-STATISTIC 2.972* 9.490*** 0.032 3.547* 5.461** 0.248

p-value (0.085) (0.002) (0.859) (0.061) (0.020) (0.619)

Sum of Spot Lags 1.022 1.172 0.991 1.023 1.118 0.9803

AIC 2.182498 2.952505 3.343235 2.184916 2.935670 3.308240

Adj. R^2 0.879 0.741 0.739 0.992 0.983 0.977

F-Statistic 431.2 95.6 14.6 5945.7 1872.7 215.8

D.W. Statistic 1.96 2.05 2.02 2.04 2.04 1.98

Table B.3 OLS Regression Results for Wholesale Price Passthrough

Weekly Prices from October 5, 1992 to June 29, 1998
Energy Information Administration/Price Changes in the Gasoline Market 43

Table B.3 (Continued)

Notes:

The general form of the linear model is shown in Equation B.1.

All prices are in cents per gallon.

“∆” is the weekly price change.

The (t), (t-1), (t-2), ..., (t-5) refer to current and lagged values.

UP refers to the weekly average price (or price change) for regular, conventional motor gasoline at U.S. Gulf Coast

(USGSPOT); or Chicago Pipeline price (CHIPIPE).

WEEKLY DUMMY refers seasonal variables, the joint test F-statistic is reported in parentheses.

AMOUNT ASYMMETRY F-STATISTIC refers to the joint hypothesis test:

∆UP(t)+∆UP(t-1)+ ... +∆UP(t-k) = 1.0

A p-value < 0.05 indicates rejection of the joint hypothesis.

Standard errors appear in parentheses below the parameter estimates.

*** indicates significant at 1% criteria (p-value < 0.01)

** indicates significant at 5% criteria (p-value < 0.05)

* indicates significant at 10% criteria (p-value < 0.10)
Appendix C

Methodology for Asymmetry Testing

Previous investigators (see BBY and BCG in Chapter 2) used a transfer function model:

y a AL y CLz BLe t o =+ + + t − t t () () () 1

where A(L), B(L), and C(L) are polynomials in the lag operator L;

and yt and zt are sequences of time series data.

This report could not use a transfer function model because the data series violated one of the

critical assumptions for transfer analysis: “that {zt} is an exogenous process that evolves

independently of the {yt} sequence. Innovations in {yt} are assumed to have no effect on the {zt}

sequence, ...”1 In the majority of market pairs in the data series, Granger Causality tests and

Variance Decomposition tests, as described by BBY, indicated bi-directional causality.

This being the case, this study did not use the model which the other studies estimated:

∆ ∆∆ DOWN C UP DOWN t =+ + ∑ ∑ β α i ti i ti − −

++ + ∑ ∑ −− − − γ δ i ti ti i ti ti U UP D DOWN et (* ) (* ) ∆ ∆ EQN C.1

where

) is the week-to-week change.

DOWNt is the downstream price at time t.

UPt is the upstream price at time t.

Ut is 1.0 if UPt > UPt-1, and 0.0 otherwise.

Dt is 1.0 if DOWNt > DOWNt-1, and 0.0 otherwise.

The summation for the UPt-i terms is from 0 to n (the lag length) and the summation for the DOWNt-i

terms is from 1 to n.

Instead of EQN C.1, after finding that multicollinearity was a significant problem and that the

asymmetry test results depended on the lag length, this study used a modified version of the BBY

Energy Information Administration/Price Changes in the Gasoline Market 45

1 Enders, Walter, Applied Econometric Time Series, Wiley (New York, 1995), p. 277.
test equation where the lags of the dependent variable were eliminated from the model specification

(i.e. a price passthrough model with asymmetry test terms):

∆ ∆ DOWN C UP ASSYM e t =+ + + ∑ ∑ β γ i ti i ti − − t EQN C.2

where

ASSYMt equals (Ut

* )UPt)

See Appendix D for the derivation. The elimination of the lagged endogenous variables decreases

the likelihood that multicollinearity will be a problem. Investigations using the transfer function

model indicated that multicollinearity was a pervasive problem, preventing reliable estimation of

the model regression coefficients. However, use of this simpler model allows only for the testing of

the downstream price variable for an asymmetrical price response from price changes in the

upstream price variable, rather than an asymmetrical response from either the upstream price

innovation or the induced downstream price response. The actual testing for pattern asymmetry

consists of a joint F-test of parameter coefficients on the ASSYM terms:

H0: all of the (i are jointly equal to zero

HA: not all the (i are equal to zero

Tables C.1 to C.3 show the parameter estimates and pattern asymmetry test results for the various

market levels. The estimation method used was Ordinary Least Squares; the AR, MA terms are

used to make the regression residuals test as random. The lag length (and AR, MA terms) was

chosen by using the estimation which minimized the Akaike Information Criterion (AIC) value; the

lag length and AR, MA terms were usually, but not always the same as those shown in Tables B.1 -

B.3.

The results of the statistical test for pattern asymmetry for the response of retail prices to changes in

upstream prices are shown in Table C.1. The test results show evidence for pervasive pattern

asymmetry for retail prices except when crude oil (WTI) spot prices is the explanatory variable.

There is no easy explanation for this seemingly anomalous lack of pattern asymmetry from WTI

prices other than that whenever weekly seasonal dummy variables were required for model

specification, the standard errors of all the estimated coefficients were uniformly larger (often by a

factor of 2) than those in estimated models which did not require the use of seasonal dummy

variables. It is possible that the seasonal component in the data adds noise to the price data and thus

reduces the testing significance relative to prices (and models) without a seasonal component.

The pattern asymmetry test results for downstream gasoline price responses to WTI crude oil price

changes are shown in Table C.2. These results show no evidence for pattern asymmetry. Table C.3

shows the test results for pattern asymmetry in the wholesale gasoline markets. This table shows

the results form both the first difference and level models of wholesale price responses. The only

market which demonstrates pattern asymmetry is the pipeline price response to USG spot price

changes, where both the level and first difference models indicate the existence of significant

46 Energy Information Administration/Price Changes in the Gasoline Market
pattern asymmetry. The other wholesale markets (USG spot→rack, and pipeline→rack) do not

indicate the presence of pattern asymmetry.

In conclusion, pattern asymmetry appears to be pervasive only for retail price responses and does

not usually occur in spot and wholesale markets. The actual importance of this finding of retail

pattern asymmetry to the idea of market imperfection is unclear, because the discussion in

Appendix A demonstrates that it may be possible for lagged downstream market price adjustments

to upstream price changes to induce price response patterns which appear pattern asymmetric.

Energy Information Administration/Price Changes in the Gasoline Market 47
48 Energy Information Administration/Price Changes in the Gasoline Market

Independent Variable WTISPOT USGSPOT CHIPIPE CHIRACK

Change in Downstream Price (dependent variable):

PADD 2 Retail Price, conventional regular gasoline

Parameter

CONSTANT 0.821 0.059 -0.099 -0.110

(0.502) (0.152) (0.085) (0.099)

∆UP(t-1) 0.249*** 0.196*** 0.222*** 0.333***

(0.083) (0.049) (0.041) (0.040)

∆UP(t-2) 0.244*** 0.279*** 0.264*** 0.149***

(0.083) (0.050) (0.047) (0.045)

∆UP(t-3) 0.022 0.173*** 0.141*** 0.048

(0.085) (0.051) (0.047) (0.045)

∆UP(t-4) 0.194** 0.114** 0.111** 0.086*

(0.088) (0.051) (0.048) (0.045)

∆UP(t-5) 0.077 0.129** 0.062 0.108**

(0.087) (0.051) (0.047) (0.045)

∆UP(t-6) 0.119 0.039 0.036 -0.043

(0.088) (0.051) (0.047) (0.045)

∆UP(t-7) -0.075 0.083* 0.007 0.148***

(0.087) (0.048) (0.047) (0.041)

∆UP(t-8) 0.182** 0.056

(0.086) (0.041)

ASSYM(t-1) 0.216 0.318*** 0.236*** 0.386***

(0.133) (0.083) (0.067) (0.065)

ASSYM(t-2) -0.085 -0.092 -0.026 -0.131*

(0.137) (0.085) (0.074) (0.067)

ASSYM(t-3) 0.085 -0.203** -0.140* 0.032

(0.137) (0.085) (0.074) (0.067)

ASSYM(t-4) -0.130 -0.027 -0.008 -0.019

(0.139) (0.086) (0.074) (0.067)

ASSYM(t-5) -0.048 -0.124 -0.075 -0.100

(0.139) (0.085) (0.074) (0.067)

ASSYM(t-6) -0.033 0.073 0.067 0.047

(0.140) (0.085) (0.074) (0.068)

ASSYM(t-7) 0.136 0.002 0.039 -0.063

(0.138) (0.082) (0.074) (0.065)

ASSYM(t-8) -0.142 0.026

(0.138) (0.067)

WEEKLY DUMMY YES** NO NO NO

(1.585)

D04OCT93 5.673*** 4.913*** 5.047*** 4.943***

(1.274) (0.887) (0.725) (0.680)

AR(1) -0.242 0.182

(0.350) (0.133)

MA(1) 0.084 -0.579*** -0.301***

(0.356) (0.110) (0.057)

OTHER ARMA TERMS AR(2,3) MA(3)

PATTERN ASYMMETRY

F-STATISTIC 0.721 3.150*** 2.346** 5.407***

p-value (0.673) (0.003) (0.019) (0.000)

AIC 3.267785 2.658656 2.425713 2.189069

Adj. R^2 0.397 0.619 0.699 0.759

F-Statistic 3.8 27.8 37.3 59.8

D.W. Statistic 1.97 1.99 2.00 1.99

Table C.1 OLS Test Equation for Retail Pattern Asymmetry

Weekly Prices from October 5, 1992 to June 29, 1998
Energy Information Administration/Price Changes in the Gasoline Market 49

Table C.1 (Continued)

Notes:

The general form of the linear model is shown in Equation C2.

All prices are in cents per gallon.

"∆" is the weekly price change.

The (t-1), (t-2), ..., (t-8) refer to lagged values.

UP refers to the weekly average price for regular, conventional motor gasoline at U.S. Gulf Coast (USGSPOT);

Chicago Pipeline price (CHIPIPE); Chicago Rack price (CHIRACK); or it refers to the weekly average spot price for

West Texas Intermediate Crude Oil (WTISPOT)

ASSYM is the defined variable to test for asymmetry.

D04OCT93 is a dummy variable corresponding to the change in Federal tax on motor gasoline.

WEEKLY DUMMY refers seasonal variables, the joint test F-statistic is reported in parentheses.

PATTERN ASYMMETRY F-STATISTIC refers to the hypothesis test: ASSYM(t)=ASSYM(t-1)=ASSYM(t-2)= ... = 0.

A p-value < 0.05 indicates rejection of the joint hypothesis.

Standard errors appear in parentheses below the parameter estimates.

*** indicates significant at 1% criteria (p-value < 0.01)

** indicates significant at 5% criteria (p-value < 0.05)

* indicates significant at 10% criteria (p-value < 0.10)
50 Energy Information Administration/Price Changes in the Gasoline Market

Independent Variable WTISPOT WTISPOT WTISPOT WTISPOT

Change in Downstream Price (dependent variable):

Parameter ∆USGSPOT ∆CHIPIPE ∆CHIRACK ∆RETPADD2

CONSTANT 0.584 0.186 1.235* 0.821

(0.638) (0.655) (0.654) (0.502)

∆WTI(t) 0.741*** 0.654*** 0.466***

(0.109) (0.101) (0.097)

∆WTI(t-1) 0.008 0.033 0.312*** 0.249***

(0.110) (0.100) (0.094) (0.083)

∆WTI(t-2) 0.213* 0.022 -0.116 0.244***

(0.110) (0.100) (0.093) (0.083)

∆WTI(t-3) 0.281** 0.446*** 0.367*** 0.022

(0.113) (0.104) (0.100) (0.085)

∆WTI(t-4) -0.068 0.194**

(0.113) (0.088)

∆WTI(t-5) -0.083 0.077

(0.111) (0.087)

∆WTI(t-6) 0.119

(0.088)

∆WTI(t-7) -0.075

(0.087)

∆WTI(t-8) 0.182**

(0.086)

ASSYM(t) 0.096 0.269* 0.261*

(0.172) (0.160) (0.154)

ASSYM(t-1) 0.099 -0.073 -0.022 0.216

(0.179) (0.158) (0.146) (0.133)

ASSYM(t-2) -0.213 0.030 0.137 -0.085

(0.176) (0.159) (0.145) (0.137)

ASSYM(t-3) -0.165 -0.277* -0.326** 0.085

(0.178) (0.166) (0.159) (0.137)

ASSYM(t-4) -0.066 -0.130

(0.181) (0.139)

ASSYM(t-5) 0.296 -0.048

(0.179) (0.139)

ASSYM(t-6) -0.033

(0.140)

ASSYM(t-7) 0.136

(0.138)

ASSYM(t-8) -0.142

(0.138)

WEEKLY DUMMY YES*** YES*** YES*** YES**

(1.656) (2.557) (1.860) (1.585)

D04OCT93 5.673***

(1.274)

AR(1) 0.225***

(0.061)

MA(1) 0.136**

(0.065)

AR(2) -0.149 0.066

(0.064) (0.066)

Table C.2 Test Equation for WTI Passthrough Pattern Asymmetry

Weekly Prices from October 5, 1992 to June 29, 1998
Energy Information Administration/Price Changes in the Gasoline Market 51

Table C.2 (Continued)

Independent Variable WTISPOT WTISPOT WTISPOT WTISPOT

Change in Downstream Price (dependent variable):

∆USGSPOT ∆CHIPIPE ∆CHIRACK ∆RETPADD2

OTHER ARMA TERMS AR(6,7,14) AR(4,6,7) AR(3,6,13) AR(3)

PATTERN ASYMMETRY F-STATISTIC 0.852 1.128 1.533 0.721

p-value (0.531) (0.344) (0.193) (0.673)

AIC 3.874764 3.847062 3.769134 3.267785

Adj. R^2 0.482 0.569 0.499 0.397

F-Statistic 5.1 7.2 5.7 3.8

D.W. Statistic 1.98 2.01 2.07 1.97

Notes:

The general form of the linear model is shown in Equation C2.

All prices are in cents per gallon.

"∆" is the weekly price change.

The (t), (t-1), ..., (t-8) refer to lagged values.

WTI refers to the weekly average spot price for West Texas Intermediate crude oil (WTISPOT).

ASSYM is the defined variable to test for asymmetry.

D04OCT93 is a dummy variable corresponding to the change in Federal tax on motor gasoline.

WEEKLY DUMMY refers seasonal variables, the joint test F-statistic is reported in parentheses.

PATTERN ASYMMETRY F-STATISTIC refers to the hypothesis test: ASSYM(t)=ASSYM(t-1)=ASSYM(t-2)= ... = 0.

A p-value < 0.05 indicates rejection of the joint hypothesis.

Standard errors appear in parentheses below the parameter estimates.

*** indicates significant at 1% criteria (p-value < 0.01)

** indicates significant at 5% criteria (p-value < 0.05)

* indicates significant at 10% criteria (p-value < 0.10)
52 Energy Information Administration/Price Changes in the Gasoline Market

Independent Variable ∆CHIPIPE

conv/reg

∆USGSPOT

conv/reg

∆USGSPOT

conv/reg

CHIPIPE

conv/reg

USGSPOT

conv/reg

USGSPOT

conv/reg

Downstream Price (dependent variable):

<-------------------PRICE CHANGE------------------>

Downstream Price (dependent variable):

<----------------PRICE LEVEL--------------->

∆CHIRACK

conv/reg

∆CHIRACK

conv/reg

∆CHIPIPE

conv/reg

CHIRACK

conv/reg

CHIRACK

conv/reg

CHIPIPE

conv/reg

CONSTANT -0.018 0.137 -0.565 1.079 -1.624 2.105

(0.034) (0.148) (0.480) (0.738) (2.803) (1.946)

UP(t) 0.580*** 0.596*** 0.767*** 0.651*** 0.658*** 0.982***

(0.034) (0.056) (0.065) (0.029) (0.048) (0.049)

UP(t-1) 0.430*** 0.425*** 0.127** 0.375*** 0.388*** 0.043

(0.034) (0.056) (0.067) (0.028) (0.045) (0.048)

UP(t-2) 0.014 -0.036 -0.007 -0.009

(0.056) (0.066) (0.044) (0.048)

UP(t-3) 0.047 0.205*** -0.011

(0.056) (0.065) (0.044)

UP(t-4) 0.038 -0.029

(0.057) (0.045)

UP(t-5) 0.127** 0.122***

(0.056) (0.047)

ASSYM(t) 0.093* 0.105 0.142 -0.002 -0.001 -0.011***

(0.056) (0.093) (0.110) (0.002) (0.004) (0.004)

ASSYM(t-1) -0.074 -0.111 -0.172 -0.001 -0.003 -0.011***

(0.056) (0.093) (0.113) (0.002) (0.005) (0.004)

ASSYM(t-2) -0.048 0.141 -0.004 -0.009***

(0.093) (0.112) (0.005) (0.002)

ASSYM(t-3) 0.034 -0.262** 0.001

(0.093) (0.110) (0.005)

ASSYM(t-4) -0.074 0.003

(0.094) (0.004)

ASSYM(t-5) -0.056 -2.8E-05

(0.093) (0.002)

WEEKLY DUMMY NO NO YES*** NO NO NO

(1.690)

AR(1) -0.104* 0.662*** .961*** 0.715***

(0.063) (0.092) (0.044) (0.050)

MA(1) -0.658*** -0.310*** 0.160**

(0.047) (0.116) (0.070)

AR(3) -0.228*** -0.235*** -0.127***

(0.061) (0.057) (0.044)

AR(6) -0.191*** -0.196*** -0.219***

(0.059) (0.058) (0.063)

Other ARMA terms AR(12) AR(2,4,5,7,9) AR(5,9) MA(12) AR(11,14),M

A(3)

PATTERN

ASYMMETRY

F-STATISTIC 1.359 0.988 2.615** 1.192 0.610 6.305***

p-value (0.259) (0.433) (0.036) (0.193) (0.723) (0.000)

AIC 2.186592 2.971967 3.325789 2.195707 2.963053 3.196083

Adj. R^2 0.879 0.741 0.746 0.992 0.983 0.977

F-Statistic 309.2 57.7 14.2 4440.4 1114.7 1138.7

D.W. Statistic 1.96 2.04 2.04 2.04 2.04 1.98

Table C.3 OLS Test Equation for Wholesale Pattern Asymmetry

Weekly Prices from October 5, 1992 to June 29, 1998
Energy Information Administration/Price Changes in the Gasoline Market 53

Table C.3 (Continued)

Notes:

The general form of the linear model is shown in Equation C.2.

All prices are in cents per gallon.

"∆" is the weekly price change.

The (t), (t-1), (t-2), ..., (t-5) refer to current and lagged values.

UP refers to the weekly average price (or price change) for regular, conventional motor gasoline at U.S. Gulf

Coast (USGSPOT); or Chicago Pipeline price (CHIPIPE).

WEEKLY DUMMY refers seasonal variables, the joint test F-statistic is reported in parentheses.

PATTERN ASYMMETRY F-STATISTIC refers to the hypothesis test: ASSYM(t)=ASSYM(t-1)=ASSYM(t-2)= ... = 0.

A p-value < 0.05 indicates rejection of the joint hypothesis.

Standard errors appear in parentheses below the parameter estimates.

*** indicates significant at 1% criteria (p-value < 0.01)

** indicates significant at 5% criteria (p-value < 0.05)

* indicates significant at 10% criteria (p-value < 0.10)
Appendix D

Derivation of Pattern Asymmetry Test Equation

The following is a demonstration of the model that was used to test for pattern and amount

asymmetry:

Let downstream (e.g. retail) motor gasoline price changes during periods of RISING upstream (e.g.

USG spot motor gasoline) prices be given by the model:

)yt =c+ α1)xt-1 + α2)xt-2 + α3)xt-3 + ... + αJ)xt-J + et EQN D.1

where )yt is the change in downstream price at time t, and )xt-i is the change in upstream price at

time period t-i. Similarly, during periods of STABLE and FALLING upstream prices the model

becomes:

)yt =c+ (1)xt-1 + (2)xt-2 + (3)xt-3 + ... + (J)xt-J + gt EQN D.2

where )yt and )xt-i are similarly defined for periods of stable or falling upstream prices. If αi = (i

for all i, then these two equations can be combined to form:

)yt =c+ $1)xt-1 + $2)xt-2 + $3)xt-3 + ... + $J)xt-J + gt. EQN D.3

where the model assumes pattern symmetry. The concept of amount symmetry can be tested by the

hypothesis 3$i = 1.0. The results of the Wald test for amount asymmetry are reported in Appendix

B; EQN B.1 has the same form as EQN D.3 with the test results shown in Tables B.1 to B.3.

However, if we assume αi  (i for some i, then EQN D.1 and D.2 can be combined to form:

)yt =c+ $1)xt-1 + $2)xt-2 + $3)xt-3 + ... + $J)xt-J EQN D.4

+ *1Dt-1)xt-1 + *2Dt-2)xt-2 + *3Dt-3)xt-3 + ... + *JDt-J)xt-J + gt.

where for all i=1,J, Dt-i = 1 if upstream prices are rising and Dt-i = 0 otherwise. A statistical test for

pattern asymmetry is to test the joint hypothesis *i = 0 for i=1,J. The results for the Wald test for

pattern asymmetry are reported in Appendix C; EQN C.2 has the same form as EQN D.4, with the

test results shown in Tables C.1 to C.3.

Energy Information Administration/Price Changes in the Gasoline Market 55