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Estimating the Price Elasticity of Gasoline Demand in Correlated Random Coefficient Models with Endogeneity

Author

Listed:
  • Michael Bates

    (Department of Economics, University of California Riverside)

  • Seolah Kim

    (Albion College)

Abstract
We propose a per-cluster instrumental variables approach (PCIV) for estimating linear correlated random coefficient models in the presence of contemporaneous endogeneity and two-way fixed effects. This approach estimates heterogeneous effects and aggregates them to population averages. We demonstrate consistency, showing robustness over standard estimators, and provide analytic standard errors for robust inference. In Monte Carlo simulation, PCIV performs relatively well in finite samples in either dimension. We apply PCIV in estimating the price elasticity of gasoline demand using state fuel taxes as instrumental variables. We find significant elasticity heterogeneity and more elastic gasoline demand on average than with standard estimators.

Suggested Citation

  • Michael Bates & Seolah Kim, 2019. "Estimating the Price Elasticity of Gasoline Demand in Correlated Random Coefficient Models with Endogeneity," Working Papers 202304, University of California at Riverside, Department of Economics, revised Aug 2023.
    • Handle: RePEc:ucr:wpaper:202304
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    as
    1. Espey, Molly, 1998. "Gasoline demand revisited: an international meta-analysis of elasticities," Energy Economics, Elsevier, vol. 20(3), pages 273-295, June.
    2. Angus Deaton, 2010. "Instruments, Randomization, and Learning about Development," Journal of Economic Literature, American Economic Association, vol. 48(2), pages 424-455, June.
    3. John Coglianese & Lucas W. Davis & Lutz Kilian & James H. Stock, 2017. "Anticipation, Tax Avoidance, and the Price Elasticity of Gasoline Demand," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(1), pages 1-15, January.
    4. Laurence Levin & Matthew S. Lewis & Frank A. Wolak, 2017. "High Frequency Evidence on the Demand for Gasoline," American Economic Journal: Economic Policy, American Economic Association, vol. 9(3), pages 314-347, August.
    5. James B. Bushnell & Erin T. Mansur & Celeste Saravia, 2008. "Vertical Arrangements, Market Structure, and Competition: An Analysis of Restructured US Electricity Markets," American Economic Review, American Economic Association, vol. 98(1), pages 237-266, March.
    6. Marshall Burke & Solomon M. Hsiang & Edward Miguel, 2015. "Global non-linear effect of temperature on economic production," Nature, Nature, vol. 527(7577), pages 235-239, November.
    7. Stefan Hoderlein & Anne Vanhems, 2018. "Estimating the distribution of welfare effects using quantiles," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(1), pages 52-72, January.
    8. James J. Heckman & Edward Vytlacil, 2005. "Structural Equations, Treatment Effects, and Econometric Policy Evaluation," Econometrica, Econometric Society, vol. 73(3), pages 669-738, May.
    9. Anthony C. Fisher & W. Michael Hanemann & Michael J. Roberts & Wolfram Schlenker, 2012. "The Economic Impacts of Climate Change: Evidence from Agricultural Output and Random Fluctuations in Weather: Comment," American Economic Review, American Economic Association, vol. 102(7), pages 3749-3760, December.
    10. Jerry A. Hausman & Whitney K. Newey, 2016. "Individual Heterogeneity and Average Welfare," Econometrica, Econometric Society, vol. 84, pages 1225-1248, May.
    11. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, April.
    12. Iván Fernández‐Val & Joonhwah Lee, 2013. "Panel data models with nonadditive unobserved heterogeneity: Estimation and inference," Quantitative Economics, Econometric Society, vol. 4(3), pages 453-481, November.
    13. Richard Blundell & Joel L. Horowitz & Matthias Parey, 2012. "Measuring the price responsiveness of gasoline demand: Economic shape restrictions and nonparametric demand estimation," Quantitative Economics, Econometric Society, vol. 3(1), pages 29-51, March.
    14. James Feyrer, 2019. "Trade and Income—Exploiting Time Series in Geography," American Economic Journal: Applied Economics, American Economic Association, vol. 11(4), pages 1-35, October.
    15. Murtazashvili, Irina & Wooldridge, Jeffrey M., 2016. "A control function approach to estimating switching regression models with endogenous explanatory variables and endogenous switching," Journal of Econometrics, Elsevier, vol. 190(2), pages 252-266.
    16. Douglas Staiger & James H. Stock, 1997. "Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, vol. 65(3), pages 557-586, May.
    17. Raj, Baldev & Srivastava, V K & Ullah, Aman, 1980. "Generalized Two Stage Least Squares Estimators for a Structural Equation with Both Fixed and Random Coefficients," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 21(1), pages 171-183, February.
    18. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-475, March.
    19. James J. Heckman & Sergio Urzua & Edward Vytlacil, 2006. "Understanding Instrumental Variables in Models with Essential Heterogeneity," The Review of Economics and Statistics, MIT Press, vol. 88(3), pages 389-432, August.
    20. Shanjun Li & Joshua Linn & Erich Muehlegger, 2014. "Gasoline Taxes and Consumer Behavior," American Economic Journal: Economic Policy, American Economic Association, vol. 6(4), pages 302-342, November.
    21. Lucas W. Davis & Lutz Kilian, 2011. "Estimating the effect of a gasoline tax on carbon emissions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(7), pages 1187-1214, November.
    22. Jeffrey M. Wooldridge, 2005. "Fixed-Effects and Related Estimators for Correlated Random-Coefficient and Treatment-Effect Panel Data Models," The Review of Economics and Statistics, MIT Press, vol. 87(2), pages 385-390, May.
    23. Jonathan E. Hughes & Christopher R. Knittel & Daniel Sperling, 2008. "Evidence of a Shift in the Short-Run Price Elasticity of Gasoline Demand," The Energy Journal, International Association for Energy Economics, vol. 29(1), pages 113-134.
    24. 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.
    25. Olivier Deschenes & Michael Greenstone & Jonathan Guryan, 2009. "Climate Change and Birth Weight," American Economic Review, American Economic Association, vol. 99(2), pages 211-217, May.
    26. Bryan S. Graham & James L. Powell, 2012. "Identification and Estimation of Average Partial Effects in “Irregular” Correlated Random Coefficient Panel Data Models," Econometrica, Econometric Society, vol. 80(5), pages 2105-2152, September.
    27. Michael David Bates & Katherine E. Castellano & Sophia Rabe-Hesketh & Anders Skrondal, 2014. "Handling Correlations Between Covariates and Random Slopes in Multilevel Models," Journal of Educational and Behavioral Statistics, , vol. 39(6), pages 524-549, December.
    28. Andreas Ravndal Kostol & Magne Mogstad, 2014. "How Financial Incentives Induce Disability Insurance Recipients to Return to Work," American Economic Review, American Economic Association, vol. 104(2), pages 624-655, February.
    29. James Heckman & Edward Vytlacil, 1998. "Instrumental Variables Methods for the Correlated Random Coefficient Model: Estimating the Average Rate of Return to Schooling When the Return is Correlated with Schooling," Journal of Human Resources, University of Wisconsin Press, vol. 33(4), pages 974-987.
    30. M. Hashem Pesaran, 2006. "Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure," Econometrica, Econometric Society, vol. 74(4), pages 967-1012, July.
    31. Gary Solon & Steven J. Haider & Jeffrey M. Wooldridge, 2015. "What Are We Weighting For?," Journal of Human Resources, University of Wisconsin Press, vol. 50(2), pages 301-316.
    32. Carol A. Dahl, 1986. "Gasoline Demand Survey," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 67-82.
    33. Baranzini, Andrea & Weber, Sylvain, 2013. "Elasticities of gasoline demand in Switzerland," Energy Policy, Elsevier, vol. 63(C), pages 674-680.
    34. Richard Tol, 2002. "Estimates of the Damage Costs of Climate Change. Part 1: Benchmark Estimates," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 21(1), pages 47-73, January.
    35. C. Kirabo Jackson & Rucker C. Johnson & Claudia Persico, 2016. "The Effects of School Spending on Educational and Economic Outcomes: Evidence from School Finance Reforms," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(1), pages 157-218.
    36. Roth, Alvin E., 1986. "Laboratory Experimentation in Economics," Economics and Philosophy, Cambridge University Press, vol. 2(2), pages 245-273, October.
    37. 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.
    38. Cragg, John G. & Donald, Stephen G., 1993. "Testing Identifiability and Specification in Instrumental Variable Models," Econometric Theory, Cambridge University Press, vol. 9(2), pages 222-240, April.
    39. Victor Chernozhukov & Iván Fernández‐Val & Jinyong Hahn & Whitney Newey, 2013. "Average and Quantile Effects in Nonseparable Panel Models," Econometrica, Econometric Society, vol. 81(2), pages 535-580, March.
    40. Callaway, Brantly & Sant’Anna, Pedro H.C., 2021. "Difference-in-Differences with multiple time periods," Journal of Econometrics, Elsevier, vol. 225(2), pages 200-230.
    41. Scott, K. Rebecca, 2012. "Rational habits in gasoline demand," Energy Economics, Elsevier, vol. 34(5), pages 1713-1723.
    42. Dahl, Carol & Sterner, Thomas, 1991. "Analysing gasoline demand elasticities: a survey," Energy Economics, Elsevier, vol. 13(3), pages 203-210, July.
    43. Jeffrey M. Wooldridge, 2008. "Instrumental variables estimation of the average treatment effect in the correlated random coefficient model," Advances in Econometrics, in: Modelling and Evaluating Treatment Effects in Econometrics, pages 93-116, Emerald Group Publishing Limited.
    44. Edward Miguel & Shanker Satyanath & Ernest Sergenti, 2004. "Economic Shocks and Civil Conflict: An Instrumental Variables Approach," Journal of Political Economy, University of Chicago Press, vol. 112(4), pages 725-753, August.
    45. Murtazashvili, Irina & Wooldridge, Jeffrey M., 2008. "Fixed effects instrumental variables estimation in correlated random coefficient panel data models," Journal of Econometrics, Elsevier, vol. 142(1), pages 539-552, January.
    46. Kelejian, Harry H, 1974. "Random Parameters in a Simultaneous Equation Framework: Identification and Estimation," Econometrica, Econometric Society, vol. 42(3), pages 517-527, May.
    47. Chamberlain, Gary, 1992. "Efficiency Bounds for Semiparametric Regression," Econometrica, Econometric Society, vol. 60(3), pages 567-596, May.
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    More about this item

    Keywords

    per cluster instrumental variables; effect heterogeneity; gasoline taxes;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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