Testing and Correcting for Endogeneity in Nonlinear Unobserved Effects Models

By Wei Lin, Jeffrey M. Wooldridge. Panel Data Econometrics, 2019

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Abstract We study testing and estimation in panel data models with two potential sources of endogeneity: correlation of covariates with time-constant, unobserved heterogeneity and correlation of covariates with time-varying idiosyncratic errors. In the linear case, we show that two control function approaches allow us to test exogeneity with respect to the idiosyncratic errors while being silent about exogeneity with respect to heterogeneity. The linear case suggests a general approach for nonlinear models. We consider two leading cases of nonlinear models: an exponential conditional mean function for nonnegative responses and a probit conditional mean function for binary or fractional responses. In the former case, we exploit the full robustness of the fixed effects Poisson quasiMLE for the probit case, we propose correlated random effects.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/a2676b3e6adb8ac730cdb459678fd5fb324374bd