A Semiparametric Analysis of Gasoline Demand in the US: Reexamining The Impact of Price
The evaluation of the impact of an increase in gasoline tax on demand relies crucially on the estimate of the price elasticity. This paper presents an extended application of the Partially Linear Additive Model (PLAM) to the analysis of gasoline demand using a panel of US households, focusing mainly on the estimation of the price elasticity. Unlike previous semi-parametric studies that use household-level data, we work with vehicle-level data within households that can potentially add richer details to the price variable. Both households and vehicles data are obtained from the Residential Transportation Energy Consumption Survey (RTECS) of 1991 and 1994, conducted by the US Energy Information Administration (EIA). As expected, the derived vehicle-based gasoline price has significant dispersion across the country and across grades of gasoline. By using a PLAM specification for gasoline demand, we obtain a measure of gasoline price elasticity that circumvents the implausible price effects reported in earlier studies. In particular, our results show the price elasticity ranges between −0.2, at low prices, and −0.5, at high prices, suggesting that households might respond differently to price changes depending on the level of price. In addition, we estimate separately the model to households that buy only regular gasoline and those that buy also midgrade/premium gasoline. The results show that the price elasticities for these groups are increasing in price and that regular households are more price sensitive compared to non-regular.
|Date of creation:||Dec 2008|
|Contact details of provider:|| Postal: Ludwigstraße 33, D-80539 Munich, Germany|
Web page: https://mpra.ub.uni-muenchen.de
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Adonis Yatchew & Joungyeo Angela No, 2001. "Household Gasoline Demand in Canada," Econometrica, Econometric Society, vol. 69(6), pages 1697-1709, November.
- Ian W. H. Parry & Kenneth A. Small, 2005.
"Does Britain or the United States Have the Right Gasoline Tax?,"
American Economic Review,
American Economic Association, vol. 95(4), pages 1276-1289, September.
- Parry, Ian & Small, Kenneth, 2002. "Does Britain or the United States Have the Right Gasoline Tax?," Discussion Papers dp-02-12-, Resources For the Future.
- Ait-Sahalia, Yacine & Bickel, Peter J. & Stoker, Thomas M., 2001. "Goodness-of-fit tests for kernel regression with an application to option implied volatilities," Journal of Econometrics, Elsevier, vol. 105(2), pages 363-412, December.
- Richard Blundell & Alan Duncan & Krishna Pendakur, 1998. "Semiparametric estimation and consumer demand," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 13(5), pages 435-461.
- Robinson, Peter M, 1988. "Root- N-Consistent Semiparametric Regression," Econometrica, Econometric Society, vol. 56(4), pages 931-54, July.
- Dahl, Carol & Sterner, Thomas, 1991. "Analysing gasoline demand elasticities: a survey," Energy Economics, Elsevier, vol. 13(3), pages 203-210, July.
- Hausman, Jerry A & Newey, Whitney K, 1995.
"Nonparametric Estimation of Exact Consumers Surplus and Deadweight Loss,"
Econometric Society, vol. 63(6), pages 1445-76, November.
- Hausman, J.A. & Newey, W.K., 1992. "Nonparametric Estimation of Exact Consumers Surplus and Deadweight Loss," Working papers 93-2, Massachusetts Institute of Technology (MIT), Department of Economics.
- Whitney K. Newey & James L. Powell & Francis Vella, 1998.
"Nonparametric Estimation of Triangular Simultaneous Equations Models,"
98-6, Massachusetts Institute of Technology (MIT), Department of Economics.
- Whitney K. Newey & James L. Powell & Francis Vella, 1999. "Nonparametric Estimation of Triangular Simultaneous Equations Models," Econometrica, Econometric Society, vol. 67(3), pages 565-604, May.
- Whitney Newey & James Powell & Francis Vella, 1998. "Nonparametric Estimation of Triangular Simultaneous Equations Models," Working papers 98-16, Massachusetts Institute of Technology (MIT), Department of Economics.
- Chamberlain, Gary, 1992. "Efficiency Bounds for Semiparametric Regression," Econometrica, Econometric Society, vol. 60(3), pages 567-96, May.
- Nicol, C. J., 2003. "Elasticities of demand for gasoline in Canada and the United States," Energy Economics, Elsevier, vol. 25(2), pages 201-214, March.
- Linton, Oliver B., 2000.
"Efficient Estimation Of Generalized Additive Nonparametric Regression Models,"
Cambridge University Press, vol. 16(04), pages 502-523, August.
- Oliver Linton, 2000. "Efficient estimation of generalized additive nonparametric regression models," LSE Research Online Documents on Economics 314, London School of Economics and Political Science, LSE Library.
- Mark Coppejans, 2003. "Flexible but Parsimonious Demand Designs: The Case of Gasoline," The Review of Economics and Statistics, MIT Press, vol. 85(3), pages 680-692, August.
- Manzan, Sebastiano & Zerom, Dawit, 2005. "Kernel estimation of a partially linear additive model," Statistics & Probability Letters, Elsevier, vol. 72(4), pages 313-322, May.
- Li, Qi, 2000. "Efficient Estimation of Additive Partially Linear Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 41(4), pages 1073-92, November.
- Daniel J. Graham & Stephen Glaister, 2002. "The Demand for Automobile Fuel: A Survey of Elasticities," Journal of Transport Economics and Policy, University of Bath, vol. 36(1), pages 1-25, January.
- Richard Blundell & Alan Duncan, 1998. "Kernel Regression in Empirical Microeconomics," Journal of Human Resources, University of Wisconsin Press, vol. 33(1), pages 62-87.
- Richard Schmalensee & Thomas M. Stoker, 1999. "Household Gasoline Demand in the United States," Econometrica, Econometric Society, vol. 67(3), pages 645-662, May.
- Hengartner, Nicolas W. & Sperlich, Stefan, 2005. "Rate optimal estimation with the integration method in the presence of many covariates," Journal of Multivariate Analysis, Elsevier, vol. 95(2), pages 246-272, August.
When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:14386. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Joachim Winter)
If references are entirely missing, you can add them using this form.