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

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.