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

Michael Bates and Seolah Kim ()

No 202021, Working Papers from University of California at Riverside, Department of Economics

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. Keywords: instrumental variables, per-cluster estimation, heterogeneous effects, population average effects, local average treatment effects.

Pages: 70 Pages
Date: 2019-08, Revised 2020-07
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https://economics.ucr.edu/repec/ucr/wpaper/202021.pdf First version, 2019 (application/pdf)
https://economics.ucr.edu/repec/ucr/wpaper/202021R.pdf Revised version, 2020 (application/pdf)

Related works:
Journal Article: Estimating the price elasticity of gasoline demand in correlated random coefficient models with endogeneity (2024)
Working Paper: Estimating the price elasticity of gasoline demand in correlated random coefficient models with endogeneity (2023)
Working Paper: Estimating the Price Elasticity of Gasoline Demand in Correlated Random Coefficient Models with Endogeneity (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:ucr:wpaper:202021

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