Estimating the price elasticity of gasoline demand in correlated random coefficient models with endogeneity

Michael Bates and Seolah Kim

Journal of Applied Econometrics, 2024, vol. 39, issue 4, 679-696

Abstract: We propose a per‐cluster instrumental variable (PCIV) approach 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.

Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1002/jae.3042

Related works:
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)
Working Paper: Estimating the Price Elasticity of Gasoline Demand in Correlated Random Coefficient Models with Endogeneity (2020)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:japmet:v:39:y:2024:i:4:p:679-696

Ordering information: This journal article can be ordered from
http://www3.intersci ... e.jsp?issn=0883-7252

Access Statistics for this article

Journal of Applied Econometrics is currently edited by M. Hashem Pesaran

More articles in Journal of Applied Econometrics from John Wiley & Sons, Ltd.
Bibliographic data for series maintained by Wiley Content Delivery ().