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

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  • Nolan Ritter

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    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.

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    File URL: http://repec.rwi-essen.de/files/REP_12_392.pdf
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    Bibliographic Info

    Paper provided by Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen in its series Ruhr Economic Papers with number 0392.

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    Length: 27 pages
    Date of creation: Dec 2012
    Date of revision:
    Handle: RePEc:rwi:repape:0392

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    Related research

    Keywords: Price elasticity; panel data; quantile regression;

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    References

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    1. Daniel J. Graham & Stephen Glaister, 2002. "The Demand for Automobile Fuel: A Survey of Elasticities," Journal of Transport Economics and Policy, London School of Economics and University of Bath, vol. 36(1), pages 1-25, January.
    2. Roger Koenker & Kevin F. Hallock, 2001. "Quantile Regression," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 143-156, Fall.
    3. 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.
    4. Wenzel Matiaske & Roland Menges & Martin Spieß, 2009. "Modifying the Rebound: It Depends!: Explaining Mobility Behaviour on the Basis of the German Socio-Economic Panel," SOEPpapers on Multidisciplinary Panel Data Research 174, DIW Berlin, The German Socio-Economic Panel (SOEP).
    5. Dahl, Carol A., 2012. "Measuring global gasoline and diesel price and income elasticities," Energy Policy, Elsevier, vol. 41(C), pages 2-13.
    6. Abrevaya, Jason & Dahl, Christian M, 2008. "The Effects of Birth Inputs on Birthweight," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 379-397.
    7. 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.
    8. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    9. Frondel, Manuel & Ritter, Nolan & Vance, Colin, 2012. "Heterogeneity in the rebound effect: Further evidence for Germany," Energy Economics, Elsevier, vol. 34(2), pages 461-467.
    10. Zia Wadud & Daniel J. Graham & Robert B. Noland, 2010. "Gasoline Demand with Heterogeneity in Household Responses," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 47-74.
    11. Chamberlain, Gary, 1982. "Multivariate regression models for panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 5-46, January.
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