Forecasting Gasoline and Diesel Prices in an Era of Rising Petroleum Prices

1. Forecasting Gasoline and Diesel Prices in an Era of Rising Petroleum Prices Vance Ginn Chapter 1 of Dissertation Texas Tech University Sam Houston State University Fall 2011 Vance.Ginn@SHSU.EDU

2. Introduction • Gasoline prices impact consumers similar to a tax. • Edelstein & Kilian (2009): SUVs and complements • Diesel prices affect decisions made by many firms. • Brown & Theis (2009): increase costs • Monetary policymakers may react to fuel prices. • Pindyck (1999): reversion to mean • Bernanke (2010, 2011): transitory, but closely watching • Friedman (1968): information about economic events • My goal is to provide good models to forecast fuel prices during different periods of volatility in an era of rising petroleum prices.

3. Literature Review • Anderson, Kellogg, and Sallee (2011) use the Michigan Survey of Consumers: what do consumers believe the price of gasoline will be in the future? • Forecast by consumers is similar to a random walk • Ginn and Gilbert (2009): prices of crude oil futures and gas • There is a 2% increase in the average weekly price of gasoline for every 10% increase in average weekly oil price futures. • There were periods that the model did not perform very well. • This lack of efficiency indicates that there may be better measures to predict gas prices. • Crude oil is the main component (42 gallons in barrel) • Chouinard and Perloff (2002): gas prices (19 gallons) • Brown and Thies (2009): diesel prices (10 gallons)

4. Gasoline and Diesel Price Components

5. Literature Review • Futures prices: • Fama and French (1987): efficient market hypothesis (EMH). • Chinn and Coibion (2010): gasoline price futures and heating oil futures are good predictors of their future spot prices. • Oil price futures appear to not be as efficient: • Alquist and Kilian (2010): not a reliable predictor in 2000s. • Buyuksahin and Harris (2011): speculators distort price futures? • Not likely, due to investors following oil market fundamentals. • Wu and McCallum (2005) show that light trading exists in longer term contracts than short-term ones, reducing the ability for prices to be valued correctly. • So the question remains, what variable(s) will provide good forecasts for future gas and diesel prices?

6. Data • I use monthly data from the EIA for the sample period January 1983 to March 2010 for the following variables: • Motor gasoline regular grade retail price (GP) (including all taxes) • On-highway diesel fuel price (DP) (including all taxes) • New York Harbor No. 2 heating oil future contract 1 (DPFut) • Imported crude oil price (OP) • Crude oil price futures (OilFut) that are traded on the New York Mercantile Exchange (NYMEX) • New York Harbor regular gasoline future contract 1 (GPFut), which is available from January 1985 to March 2010. • Split into two types: • Reformulated regular gasoline: January 1985 to December 2006 • Reformulated gasoline blendstock for oxygenate blending (RBOB) includes a percentage of ethanol that was added to gasoline in 2005: January 2007 to March 2010.

7. An Era of Rising Petro Prices

8. Table 1: Statistics for Variables for Estimation Period from1983:1-2002:12 Summary StatsCorrelation Stats Table 2: Statistics for Variables for Forecast Period from 2003:1-2010:3 Summary Stats Correlation Stats

9. Table 3: Augmented Dickey-Fuller Unit Root Tests Null Hypotheses: GP, GPFut, DP, DPFut, OP, OilFut has a unit root Exogenous: Constant, Lag Length: Automatic selection based on AIC, max lag is 14) Table 4: Phillips-Perron Unit Root Tests Null Hypotheses: GP, GPFut, DP, DPFut, OP, OilFut has a unit root Exogenous: Constant, Bandwidth: (Newey-West automation) using Bartlett kernel Note: The sample period is 1983:1-2002:12, except for gasoline price futures (GPFut) which is from 1985:1-2002:12.

10. Table 5: Engle-Granger Cointegration Tests *MacKinnon (1996) p-values. Notes: The data are in logs and the sample period is 1983:1-2002:12, except for gasoline price futures (GPFut) which is from 1985:1-2002:12. The null hypothesis is that the series are not cointegrated. The automatic lag specification is based on the Schwarz Bayesian Criterion (SBC).

11. Table 7: Forecast Model Representations

12. Table 10: GasOut-of-Sample Rolling Forecast RMSEs

13. Table 11: DieselOut-of-Sample Rolling Forecast RMSEs

14. Rolling Out-of-Sample Forecasts: 2003:1-2004:12 GP Forecast Using GPFut DP Using ARCH(DPFut)

15. Rolling Out-of-Sample Forecasts: 2005:6-2007:5 GP Forecast Using GPFut DP Using ARCH(DPFut)

16. Rolling Out-of-Sample Forecasts: 2008:4-2010:3 GP Forecast Using GPFut DP Using OP

17. Conclusions • No matter whether gas prices are stable or not, their futures price does better at forecasting them than others. • Fama(1970): Efficient Market Hypothesis • Similarly, diesel price futures perform well at predicting diesel prices during periods of stable increases and shocks, but spot oil prices helped predict prices during the latter period. • Therefore, I construct good models to forecast fuel prices during recent periods of rising petroleum prices that achieves a further understanding of their future prices.

18. Future Research • What do futures prices tell us about business cycles? • What are the impacts on the term structure of interest rates from gas and diesel price shocks? • What implications are there for monetary policy? • Do asymmetric responses exist between fuel price futures and the price at the pump?