Manzan, sebastiano and Zerom, Dawit (2008): A Semiparametric Analysis of Gasoline Demand in the US: Reexamining The Impact of Price. Forthcoming in: Econometric Reviews

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Abstract
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 semiparametric studies that use householdlevel data, we work with vehiclelevel 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 vehiclebased 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 nonregular.
Item Type:  MPRA Paper 

Original Title:  A Semiparametric Analysis of Gasoline Demand in the US: Reexamining The Impact of Price 
Language:  English 
Keywords:  semiparametric methods; partially linear additive model; gasoline demand 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C14  Semiparametric and Nonparametric Methods: General D  Microeconomics > D1  Household Behavior and Family Economics > D12  Consumer Economics: Empirical Analysis 
Item ID:  14386 
Depositing User:  Dawit Zerom 
Date Deposited:  01. Apr 2009 04:40 
Last Modified:  12. Feb 2013 19:54 
References:  A¨ıt Sahalia, Y., Bickel, P.J. and Stoker, T.M. (2001). Goodnessoffit tests for regression using kernel methods. Journal of Econometrics, 105, 363–412. Blundell, R. and Duncan, A. (1998). Kernel regression in empirical microeconomics. Journal of Human Resources, 33, 62–87. Blundell, R., Duncan, A. and Pendakur, K. (1998). Semiparametric estimation and consumer demand. Journal of Applied Econometrics, 13, 435–461. Chamberlain, G. (1992). Efficiency bounds for semiparametric regression. Econometrica, 60, 567–596. Congressional Budget Office (2002). Reducing gasoline consumption: three policy options. CBO study. Congressional Budget Office (2003). The economic costs of fuel economy standards versus a gasoline tax. CBO study. Coppejans, M. (2003). Flexible but parsimonious demand designs: The case of gasoline. Review of Economics and Statistics, 85, 680–692. Dahl, C. and Sterner, T. (1991). Analysing gasoline demand elasticities: a survey. Energy Economics, 13, 203–210. Fan, J., H¨ardle, W. and Mammen, E. (1998). Direct estimation of low dimensional components in additive models. Annals of Statistics, 26, 943–971. Fan, Y. and Li, Q. (2003). A kernelbased method for estimating additive partially linear models. Statistica Sinica, 13, 739–762. Graham, D.J. and Glaister, S. (2002). The demand for automobile fuel: A survey of elasticities. Journal of Transportation Economics and Policy, 36, 1–26. Hausman, J.A. and Newey, W.K. (1995). Nonparametric estimation of exact consumers surplus and deadweight loss. Econometrica, 63, 1445–1476. Hengartner, N.W. and Sperlich, S. (2005). Rate optimal estimation with the integration method in the presence of many covariates. Journal of Multivariate Analysis, 95, 246– 272. Kim, W., Linton, O.B. and Hengartner, N.W. (1999). A computationally efficient oracle estimator for additive nonparametric regression with bootstrap confidence intervals. Journal of Computational and Graphical Statistics, 8, 278–297. Li, Q. (2000). Efficient estimation of additive partial linear models. International Economic Review, 41, 1073–1091. Linton, O.B. (1996). Efficient estimation of additive nonparametric regression models. Biometrika, 84, 469–474. Liu, R.Y. (1988). Bootstrap procedure under some noni.i.d. models. Annals of Statistics, 16, 1696–1708. 24 Manzan, S. and Zerom, D. (2005). Kernel estimation of a partially linear additive model. Statistics & Probability Letters, 72, 313–322. Moral, I. and RodriguezPoo, J.M. (2004). An efficient marginal integration estimator of a semiparametric additive modeling. Statistics and Probability Letters, 69, 451–463. Newey, W., Powell, J. and Vella, F. (1999). Nonparametric estimation of triangular simultaneous equations models. Econometrica, 67, number 3, 565–603. Nicol, C.J. (2003). Elasticities of demand for gasoline in Canada and the United States. Energy Economics, 25, 201–214. Parry, I.W.H. and Small, K.A. (2005). Does Britain or the United States have the right gasoline tax? American Economic Review, 95, 1276–1289. Robinson, P. (1988). Rootn consistent semiparametric regression. Econometrica, 56, 931–954. Schmalensee, R. and Stoker, T.M. (1999). Household gasoline demand in the United States. Econometrica, 67, 645–662. US Department of Energy (1996). Policies and measures for reducing energy related greenhouse gas emissions: lessons from recent literature. Report No. DOE/PO0047. US Department of Energy (2004). Emissions of greenhouse gases in the United States 2003. Report No. DOE/EIA0573. Yatchew, A. and No, J.A. (2001). Household gasoline demand in Canada. Econometrica, 69, 1697–1709. 25 
URI:  http://mpra.ub.unimuenchen.de/id/eprint/14386 