Estimation of Dynamic Panel Data Sample Selection Models

Ekaterini Kyriazidou

99 citations0/0 files storedThe Review of Economic Studies

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Spatial and Panel Data Analysis Statistical Methods and Bayesian Inference Advanced Causal Inference Techniques

Abstract

This paper considers the problem of identification and estimation in panel data sample selection models with a binary selection rule, when the latent equations contain strictly exogenous variables, lags of the dependent variables, and unobserved individual effects. We derive a set of conditional moment restrictions which are then exploited to construct two-step GMM-type estimators for the parameters of the main equation. In the first step, the unknown parameters of the selection equation are consistently estimated. In the second step, these estimates are used to construct kernel weights in a manner such that the weight that any two-period individual observation receives in the estimation varies inversely with the relative magnitude of the sample selection effect in the two periods. Under appropriate assumptions, these "kernel-weighted" GMM estimators are consistent and asymptotically normal. The finite sample properties of the proposed estimators are investigated in a small Monte-Carlo study.

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References

1991Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations 1995Another look at the instrumental variable estimation of error-components models 1995Initial conditions and moment restrictions in dynamic panel data models 2003Analysis of Panel Data 1982Time to Build and Aggregate Fluctuations 1988Estimating Vector Autoregressions with Panel Data 1981Estimation of Dynamic Models with Error Components 1995Efficient estimation of models for dynamic panel data

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