# Presentation: 799073

## Natural Gas Prices and Industrial Sector Consumption Responses:An Experimental Module for STIFS

- Frederick L. Joutz, Associate Professor
- Department of Economics
- The George Washington University
- Washington, Dc 20052
- (202) 994-4899
- [e-mail _[bmark@gwu.edu](mailto:bmark@gwu.edu)__] _

- Dave Costello
- Office of Energy Markets and End Use (EMEU)
- Energy Information Administration
- U.S. Department of Energy
- (202-586-1468),
- [e-mail dave.costello@eia.doe.gov]

## Objective

- The Short-Term Integrated Forecasting System (STIFS) generates monthly forecasts of energy demand, supply and prices using some forecast information that is incorporated into STIFS that is generated by other models that do not run in an integrated framework with STIFS.
- This project tests an experimental model for the interaction between natural gas prices, natural gas consumption, and industrial sector activity.

## Strategy 

- The approach taken here involves a simple VAR framework capturing the time series dynamics testing for Granger causality and examining impulse response functions and forecast error variance decompositions.
- In another approach (which won’t be treated in detail here), energy and economic variables are analyzed in terms of integration, cointegration for a long-run relationship between oil and natural gas prices.
- The general to specific modeling methodology is used to develop a data coherent parsimonious representation. Issues related to parameter constancy, encompassing, and forecasting are discussed.
- Ultimately, the forecasting performance of the two strategies will be compared.

## Industrial Natural Gas Price

## “Net” Industrial Gas Price

- “Net” Industrial Gas Price

## Gas-Intensive Industrial Output Index

## Industrial Gas Demand and Gas-Intensive Industrial Output Index

## The General Form of the Vector Autoregressive (VAR) system that captures the extent of feedback between the industrial sectors and (industrial) natural gas prices is:

- ngicuus(p) – nominal price of natural gas to industry deflated by the producer price index for non-energy and food products, $/MCF (1982-84=0).
- pgi_n(net) - Hamilton “net price” of industrial natural gas, the difference between the highest monthly price in the last three years and the current price.
- nginx(c) -  natural gas demand by industry including CHP, BCFD.
- gbig6(y) - is the industrial production index for the six largest industrial sectors consuming 80% of that sector’s natural gas.

## Econometric Model Design

- Specification Tests – Alternative Models
- Integration - Cointegration
- Lag Length
- Residual Diagnostics AIC, BC, HQ, ACFs
- Normality
- Granger Causality
- Impulse Response
- Forecast Error Variance Decomposition
- Simulation
- Error Correction Modeling
- Further Forecast Comparisons

## Table 3.a Specification Selection from Alternative VAR Models with Net Price: Sample 1989.01 – 2003.08

- The five alternative models are:
- No seasonal and event dummies and exogenous variables
- No event dummies and exogenous variables
- No exogenous variables
- No event dummies
- Include  seasonal and event dummies and exogenous variables

## Table 3.a Specification Selection from Alternative VAR Models with Net Price: Sample 1989.01 – 2003.08

**Model Reduction via F-test**
- MODEL(5) --> MODEL(4): F(32,444)=   1.17 [0.2382]
- MODEL(5) --> MODEL(3): F(40,456)=   2.26 [0.0000]**
- MODEL(5) --> MODEL(2): F(52,466)=   1.95 [0.0002]**
- MODEL(5) --> MODEL(1): F(96,477)=   3.78 [0.0000]**
- MODEL(4) --> MODEL(3): F(8,256) =    6.82 [0.0000]**
- MODEL(4) --> MODEL(2): F(20,425)=   3.18 [0.0000]**
- MODEL(4) --> MODEL(1): F(64,503)=   5.05 [0.0000]**
- MODEL(3) --> MODEL(2): F(12,344)=  0.83  [0.6104]
- MODEL(3) --> MODEL(1): F(56,507)=   4.45 [0.0000]**
- MODEL(2) --> MODEL(1): F(44,510)=   5.48 [0.0000]**
- 		[  ]  p-value

## Slide 12

| Table 18: Granger “Causality” Tests: 4 Variable Model
Sample 1989.01 2003.08 (176 observations) |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- |
|  | Dependent Variable |  |  |  |  |
| Exclusion Restriction
p-values |  | LNGICUUS | Net Price | LNGINX | LGBIG6 |
|  | LNGICUUS | -- | 0.00 | 0.347 | 0.324 |
|  | Net Price | 0.113 | -- | 0.158 | 0.067 |
|  | LNGINX | 0.049 | 0.013 | -- | 0.728 |
|  | LGBIG6 | 0.002 | 0.005 | 0.130 | -- |
|  | All | 0.000 | 0.00 | 0.044 | 0.5934 |

## Slide 13

## Forecast Model Simulation

- How does model fit or track the turbulent period?
- Crude Simulation starting January 1989.
- Dynamic for endogenous variables
- Actual values for exogenous variables
- Fit first through sample, then only through December 1997

## Forecast Simulation Levels

## Slide 16

## Summary

- Tests an experimental model for the interaction between natural gas prices, natural gas consumption, and industrial sector activity.
- Use a VAR framework
- Incorporate a “net” natural gas price measure for memory of shocks
- Impulse response function suggests industrial natural gas price shocks have a negative impact on industrial sector.
- Simulation exercise suggests VAR model reasonably approximates the DGP....

## Questions for the Committee

**Methodology: VAR Model of Industrial Natural Gas Price, Industrial Natural Gas Consumption, Industrial Production Interactions**
- Does the VAR approach to modeling relationships between the variables appear fruitful? What are the strengths and weaknesses of the approach?
- Should VAR models for other sectors and fuels be considered? Which ones?
- Are there other “net” price variables that you think should be considered? Are their better types of shock variables?
- Is modeling the natural gas consumption of the BIG 6 industrial sectors appropriate?
- They are currently weighted by value-added in 2000. Are there other weighting schemes which should be considered like natural gas consumption weights?

## A VAR with lag length (or order) p can be expressed as

- Where
- Y   is (n * 1) the number of variables
- A0 is (n * 1) the matrix of intercepts and deterministic variables (n * 1)	the intercept plus seasonal dummies and or trend variables
- Ai is  (n * n) matrix of coefficients at lag i
- et  is  (n * 1) the vector of error terms with mean 0 and contemporaneous variance covariance matrix X
- 	is (m * 1)	the set of exogenous variables
- B(L) Lag operator for exogenous variables

## A(L) is a matrix polynomial lag operator 	where each matrix is 4x4  

- D is a 4x12 matrix of a constant and coefficients for 11 monthly dummy variables in each equation with December as the constant.
- C is a 4x3 matrix of coefficients for the three extreme events. This matrix can be expanded later to include more events.
- B is 4x5 matrix for the vector, x, of exogenous variables.
- The vector *              *is the vector of white noise residual terms which may be contemporaneously correlated. The symbols (p, net, c, and y) represent the price, “net price”, consumption, and industrial production respectively.

- ~

## Lag Length Selection for the VAR

- The Bayesian Schwartz Criterion (BSC), the Akaike Information Criterion (AIC), and the Hannan-Quinn Criterion are used as alternative criterion.  They rely on information similar to the Chi-Squared tests and are derived as follows:

## Table 3.a Specification Selection from Alternative VAR Models with Net Price: Sample 1989.01 – 2003.08

| Model | Log
Likelihood | AIC | Schwartz | HQ |
| --- | --- | --- | --- | --- |
| 1 | 1475.6556 | -15.451 | -13.361 | -14.603 |
| 2 | 1605.8456 | -16.430 | -13.548 | -15.261 |
| 3 | 1612.5548 | -16.370 | -13.272 | -15.113 |
| 4 | 1646.5670 | -16.666 | -13.423 | -15.350 |
| 5 | 1672.9467 | -16.602 | -12.783 | -15.503 |

## Selection Process

- Intuitively, the log determinant will decline as the number of regressors increases, just as in a single equation ordinary
- least squares regression.  It is similar to the residual sum of squares or estimated variance. The second term on the
- right hand side acts as a penalty for including additional regressors; it increases the statistic.  We calculate these statistics for each lag length and choose the lag length based on the model(s) with the minimum value for the statistics.  The three tests do not always agree as to the optimal lag lengths. The AIC is biased towards selecting more lags than is actually needed, but this is not necessarily bad.

## Table 11. Residual Correlation Matrix – 4-variable Model 

|  | LNGICUUS | LPGI_N
 | LNGINX | LGBIG6 |
| --- | --- | --- | --- | --- |
| LNGICUUS | 1.0000 | -0.7246 | -0.2059 | 0.0343
 |
| LPGI_N | -0.7246 | 1.0000 | 0.0679 | -0.1474 |
| LNGINX
 | -0.2059 | 0.0679 | 1.0000 |  0.1524 |
| LGBIG6 | 0.0343 | -0.1474 | 0.1524 | 1.0000 |

## Ordering of Impulses for Response Analysis

_**Ordering of Impulses for Response Analysis**_

## Slide 26

## Slide 27

| Name | Description and Units | Source |
| --- | --- | --- |
| NGICUUS | nominal price of natural gas to industry, $/MCF. | EIA |
| NGICUUSR | real price of natural gas to industry deflated by the producer price index for non-energy and food products, $/MCF, WPIINUS (1982-84=0). | EIA |
| PGI_N | Net Industrial Natural Gas Price Measure, the difference between the highest monthly price in the last three years and the current price. | EIA |
| NGINX | Natural gas demand: industrial sector (Incl CHP), BCFD, NSA | EIA |
| GBIG6 | Weighted Industrial Production Index for six largest natural gas consuming 2-digit sectors, consuming 80% of that sector’s natural gas.
Weights by value-added in 2000
0.0994*g331+0.2760*g311+0.1176*g322+0.3544*g325+0.0838*g327+0.06886*g324, NSA, 1997=100 | Federal Reserve Board |

## Slide 28

| Name | Description and Units | Source |
| --- | --- | --- |
| NGSPRFTC | the lagged relative price of, ngspuus, to the lagged rftcuus, converted to MCF. The spot price, (in DMMB) is divided by 1.03 and the fuel price (in $/G) is divided by 6.287.  | EIA |
| RFTCEUS | price of retail residual fuel price, $/MMBTU | EIA |
| NGSPUUS | spot natural gas, $/TCF | EIA |
| RFEUDUS | the price of residual fuel oil to electric utilities, ($/MMBTU) | EIA |
| x(4) | the deviation of heating degree days from normal, (ZGHDPUS-ZGHNPUS). They are population weighted and divided by the number of days in the month, ZSAJQUS. | NOAA |
| ZWHDPUS | Monthly population weighted degree days | NOAA |
| ZWHNPUS | Monthly population weighted normal heating degree-days. Normal = trend-adjusted 1971-2000 average. | NOAA |
| x(5) | Interaction term between cold weather monthly dummy variables and deviations of heating degree days from normal. The monthly variables are October through April. |  |

## Slide 29

| Name | Description and Units | Source |
| --- | --- | --- |
| ZSAJQUS | The number of days per month |  |
| GASVAR | deviation of natural gas from normal storage levels (BCFD) | EIA |
| D_HAND | Dummy variable for Hurricane Andrew which caused production shutdowns in September and October 1992. | EIA |
| D_9402 | Dummy variable for February 1994 as a result of cold weather, ice storms, and transmission problems. | EIA |
| D_9602 | Dummy variable for February 1996 for expectations of cold weather and low inventories leading to panic in the spot price market. | EIA |

- Six of the most energy consumptive industry groups - chemicals, petroleum refining, primary metals, paper, food processing, and nonmetallic minerals (glass, lime, cement, etc.) - lead the way in natural gas consumption.  Among those, the chemical industry is dominant.  According to the 1998 Manufacturing Energy Consumption Survey (MECS), the chemical industry used 36 percent of all natural gas consumed by all U.S. manufacturers in 1998 (and in fact, 12 percent of TOTAL U.S. gas consumption for all purposes). Together, the top six industry groups accounted for 84 percent of manufacturing use of natural gas, so most of the aggregate economic effect of natural gas price trends would be evident in those industries.

## Slide 31

| BIG 6 Natural Gas Consuming Sectors |  |  | Value added in 2000 |  |  |  |
| --- | --- | --- | --- | --- | --- | --- |
| Groups and Series | NAICS |  | Millions of Dollars | Proportion | Type of Series | Percent of Big 6 |
| Manufacturing (NAICS) |  |  | 2,011,258 | 80.57 |  | 0.099417 |
| Primary Metal | 331 |  | 66095 | 2.65 | Prod | 0.099417 |
| Food | 311 |  | 183,482 | 7.35 |  | 0.275985 |
| Paper | 322 |  | 78,166 | 3.13 | Prod | 0.117573 |
| Chemical | 325 |  | 235,614 | 9.44 | Prod | 0.354399 |
| Nonmetallic mineral product | 327 |  | 55,722 | 2.23 | Prod | 0.083814 |
| Petroleum and coal products | 324 |  | 45,748 | 1.83 | Prod | 0.068812 |
| Total Big Six |  |  | 664827 | 26.63 |  | 0.330553 |
| Fertilizer | 32531 |  | 3,420 | 0.14 | Qprod |  |

## Recursive Analysis

- The estimation sample begins in January 1989 and the model is fit through December 1994 before the recursive analysis begins. The graphs for the four-equation VAR system are provided; there is little difference with the three equation VAR system. (They are available upon request.) By construction the equations for the industrial natural gas price and the “net” natural gas price are going to be unstable. In the figures, the test statistics are normalized by the critical values. Thus, when the lines exceed unity it represents a rejection of the null hypothesis of insignificant errors or model stability. A single graph is shown for each equation and the fifth graph is for the system as a whole.
- The 1-step Chow graphs show only a few rejections for the price and net price equations out of 104 tests. These occur in 1997, 1999, 2000, and 2003.
- The N-step down tests have a similar but stronger pattern. The equation estimates for the industrial natural gas price and the “net” natural gas price show rejections up until the last few months. The models appear unstable, because of the tremendous instability in the natural gas price series in the last year but also in 2000. This is driven by the price movements and heights which had previously not been realized. If the recursive tests were performed on shorter samples, the models would have appeared (more) stable. There are no rejections for the natural gas consumption equation and industrial production equation. The graph for the system as a whole shows the same rejection pattern as the price equations.

## Slide 33

## Slide 34

## Slide 35

## Forecast Simulation Percent Deviation from Actual