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Gasoline demand in Europe: New insights

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  • Pock, Markus

Abstract

This study utilizes a panel data set from 14 European countries over the period 1990-2004 to estimate a dynamic model specification for gasoline demand. Previous studies estimating gasoline consumption per total passenger cars ignore the recent increase in the number of diesel cars in most European countries leading to biased elasticity estimates. We apply several common dynamic panel estimators to our small sample. Results show that specifications neglecting the share of diesel cars overestimate short-run income, price and car ownership elasticities. It appears that the results of standard pooled estimators are more reliable than common IV/GMM estimators applied to our small data set.

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  • Pock, Markus, 2010. "Gasoline demand in Europe: New insights," Energy Economics, Elsevier, vol. 32(1), pages 54-62, January.
    • Handle: RePEc:eee:eneeco:v:32:y:2010:i:1:p:54-62
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    1. Espey, Molly, 1998. "Gasoline demand revisited: an international meta-analysis of elasticities," Energy Economics, Elsevier, vol. 20(3), pages 273-295, June.
    2. David Roodman, 2009. "How to do xtabond2: An introduction to difference and system GMM in Stata," Stata Journal, StataCorp LP, vol. 9(1), pages 86-136, March.
    3. Badi H. Baltagi & Georges Bresson & James M. Griffin & Alain Pirotte, 2003. "Homogeneous, heterogeneous or shrinkage estimators? Some empirical evidence from French regional gasoline consumption," Empirical Economics, Springer, vol. 28(4), pages 795-811, November.
    4. 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.
    5. Pesaran, M. Hashem & Smith, Ron, 1995. "Estimating long-run relationships from dynamic heterogeneous panels," Journal of Econometrics, Elsevier, vol. 68(1), pages 79-113, July.
    6. Mayeres, Inge & Proost, Stef, 2001. "Should diesel cars in Europe be discouraged?," Regional Science and Urban Economics, Elsevier, vol. 31(4), pages 453-470, July.
    7. Bun, Maurice J. G. & Kiviet, Jan F., 2003. "On the diminishing returns of higher-order terms in asymptotic expansions of bias," Economics Letters, Elsevier, vol. 79(2), pages 145-152, May.
    8. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    9. Baltagi, Badi H. & Li, Qi, 1995. "Testing AR(1) against MA(1) disturbances in an error component model," Journal of Econometrics, Elsevier, vol. 68(1), pages 133-151, July.
    10. Bun, Maurice J.G. & Kiviet, Jan F., 2006. "The effects of dynamic feedbacks on LS and MM estimator accuracy in panel data models," Journal of Econometrics, Elsevier, vol. 132(2), pages 409-444, June.
    11. David Roodman, 2006. "How to Do xtabond2," North American Stata Users' Group Meetings 2006 8, Stata Users Group.
    12. Baltagi, Badi H. & Griffin, James M., 1997. "Pooled estimators vs. their heterogeneous counterparts in the context of dynamic demand for gasoline," Journal of Econometrics, Elsevier, vol. 77(2), pages 303-327, April.
    13. Maddala, G S, et al, 1997. "Estimation of Short-Run and Long-Run Elasticities of Energy Demand from Panel Data Using Shrinkage Estimators," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(1), pages 90-100, January.
    14. Kiviet, Jan F., 1995. "On bias, inconsistency, and efficiency of various estimators in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 68(1), pages 53-78, July.
    15. N. Künzli & R. Kaiser & S. Medina & M. Studnicka & O. Chanel & P. Filliger & M. Herry & F. Horak & V. Puybonnieux-Texier & Philippe Quénel & Jodi Schneider & R. Seethaler & Jean-Christophe Vergnaud & , 2000. "Public health Impact of Outdoor and Traffic related Air Pollution," Post-Print halshs-00150955, HAL.
    16. Carol A. Dahl, 1986. "Gasoline Demand Survey," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 67-82.
    17. Baltagi, Badi H. & Griffin, James M., 1983. "Gasoline demand in the OECD : An application of pooling and testing procedures," European Economic Review, Elsevier, vol. 22(2), pages 117-137, July.
    18. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-1426, November.
    19. Zachariadis, Theodoros, 2006. "On the baseline evolution of automobile fuel economy in Europe," Energy Policy, Elsevier, vol. 34(14), pages 1773-1785, September.
    20. Dahl, Carol & Sterner, Thomas, 1991. "Analysing gasoline demand elasticities: a survey," Energy Economics, Elsevier, vol. 13(3), pages 203-210, July.
    21. P. Filliger & M. Herry & F. Horak & V. Puybonnieux-Texier & P. Quenel & J. Schneider & R.K. Seethaler & J.C. Vernaud & H. Sommer & N. Künzli & R. Kaiser & S. Medina & M. Studnicka & Olivier Chanel, 2000. "Public-health impact of outdoor and traffic-related air pollution: a European assessment," Post-Print hal-01462907, HAL.
    22. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
    23. Judson, Ruth A. & Owen, Ann L., 1999. "Estimating dynamic panel data models: a guide for macroeconomists," Economics Letters, Elsevier, vol. 65(1), pages 9-15, October.
    24. Richard Schmalensee & Thomas M. Stoker, 1999. "Household Gasoline Demand in the United States," Econometrica, Econometric Society, vol. 67(3), pages 645-662, May.
    25. Anderson, T. W. & Hsiao, Cheng, 1982. "Formulation and estimation of dynamic models using panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 47-82, January.
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