Estimation of Nonlinear Dynamic Panel Data Models with Attrition - Anastasia Semykina

Title: Estimation of Nonlinear Dynamic Panel Data Models with Attrition

Abstract: We present a general framework for nonlinear dynamic panel data models subject to missing out- comes due to endogenous attrition. We consider two cases of attrition. First, ignorable attrition where the distribution of the outcome does not depend on missingness conditional on unobserved individual heterogeneity. Second, non-ignorable attrition where the conditional distribution of the outcome does depend on attrition. In either case, a major challenge posed by the dynamic specification is the inherent correlation between lagged dependent variable and the unobserved individual heterogeneity. Our key assumption is that the distribution of the unobserved heterogeneity does not depend on attrition once conditioning on observed covariates and initial condition. The resulting estimator is a joint MLE that accommodates a dynamic specification, correlated unobserved heterogeneity, and endogenous attrition. We discuss the derivation and estimation of the average partial effects within this framework and provide examples for the binary response, ordinal response, and corner solution cases. The proposed method is applied to a dynamic health model among older women.