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Beyond the Average Elasticity – Applying Quantile Panel Regression to German Household Mobility Data

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  • Ritter, Nolan

Abstract

This paper employs quantile panel regression to the study of fuel price elasticities. Contrasting with standard panel approaches, this method reveals the impact of explanatory variables across all points in the conditional distribution of the response variable while controlling for unobserved heterogeneity. Applying quantile panel regression to German household data demonstrates that fuel price elasticities are very high in magnitude - below -0.8 - for a small segment of households whose car mileage is low, but that this effect tapers off rapidly among households with higher car mileage. These findings have implications for policy instruments that rely on estimates of fuel price elasticities, for example fuel taxation.

Suggested Citation

  • Ritter, Nolan, 2012. "Beyond the Average Elasticity – Applying Quantile Panel Regression to German Household Mobility Data," Ruhr Economic Papers 392, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    • Handle: RePEc:zbw:rwirep:392
    DOI: 10.4419/86788447
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    References listed on IDEAS

    as
    1. 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.
    2. Chamberlain, Gary, 1982. "Multivariate regression models for panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 5-46, January.
    3. Frondel, Manuel & Vance, Colin, 2010. "Driving for fun? Comparing the effect of fuel prices on weekday and weekend fuel consumption," Energy Economics, Elsevier, vol. 32(1), pages 102-109, January.
    4. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    5. Matiaske, Wenzel & Menges, Roland & Spiess, Martin, 2012. "Modifying the rebound: It depends! Explaining mobility behavior on the basis of the German socio-economic panel," Energy Policy, Elsevier, vol. 41(C), pages 29-35.
    6. Dahl, Carol A., 2012. "Measuring global gasoline and diesel price and income elasticities," Energy Policy, Elsevier, vol. 41(C), pages 2-13.
    7. repec:aen:journl:2008v29-04-a07 is not listed on IDEAS
    8. repec:aen:journl:2010v31-01-a03 is not listed on IDEAS
    9. Roger Koenker & Kevin F. Hallock, 2001. "Quantile Regression," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 143-156, Fall.
    10. Manuel Frondel & Jorg Peters & Colin Vance, 2008. "Identifying the Rebound: Evidence from a German Household Panel," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 145-164.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

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    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • D13 - Microeconomics - - Household Behavior - - - Household Production and Intrahouse Allocation
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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