Estimating a Censored Dynamic Panel Data Model With an Application to Earnings Dynamics

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Abstract
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. The estimation method is used to study earnings dynamics using the matched data from the Current Population Survey and Social Security Administration (CPS-SSA) Earnings Records.

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