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

Michael Bates and Seolah Kim ()
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Seolah Kim: Albion College

No 202304, 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: per cluster instrumental variables; effect heterogeneity; gasoline taxes (search for similar items in EconPapers)
JEL-codes: C33 C36 C38 Q41 Q54 (search for similar items in EconPapers)
Pages: 62 Pages
Date: 2019-08, Revised 2023-08
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https://economics.ucr.edu/repec/ucr/wpaper/202304.pdf First version, 2019 (application/pdf)
https://economics.ucr.edu/repec/ucr/wpaper/202304R.pdf Revised version, 2023 (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 (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:ucr:wpaper:202304

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