Instrumental variables estimation of a generalized correlated random coefficients model

Matthew Masten and Alexander Torgovitsky

No CWP02/14, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

Abstract: We study identi?cation and estimation of the average treatment effect in a correlated random coefficients model that allows for ?rst stage heterogeneity and binary instruments. The model also allows for multiple endogenous variables and interactions between endogenous variables and covariates. Our identi?cation approach is based on averaging the coefficients obtained from a collection of ordinary linear regressions that condition on different realizations of a control function. This identi?cation strategy suggests a transparent and computationally straightforward estimator of a trimmed average treatment effect constructed as the average of kernel-weighted linear regres-sions. We develop this estimator and establish its vn–consistency and asymptotic normality. Monte Carlo simulations show excellent ?nite-sample performance that is comparable in precision to the standard two-stage least squares estimator. We apply our results to analyze the effect of air pollution on house prices, and ?nd substantial heterogeneity in ?rst stage instrument effects as well as heterogeneity in treatment effects that is consistent with household sorting.

Date: 2014-01-08
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Citations: View citations in EconPapers (12)

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Working Paper: Instrumental variables estimation of a generalized correlated random coefficients model (2014)
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