Estimation of a Censored Dynamic Panel Data Model
نویسنده
چکیده
This paper proposes a method for estimating a censored panel data model with a lagged latent dependent variable and individual-specific fixed effects. The main insight is to trim observations in such a way that a certain symmetry, which was destroyed by censoring, is restored. Based on the restored symmetry, orthogonality conditions are constructed and GMM estimation is implemented. Valid asymptotic confidence intervals can be constructed for the parameters of interest. The proposed method is illustrated by estimating a dynamic earnings model using top-coded social security earnings data.
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تاریخ انتشار 2000