Nonparametric Identification in Dynamic Nonseparable Panel Data Models

We consider the identification of covariate-conditioned and average partial effects in dynamic nonseparable panel data structures. We demonstrate that a control function approach is sufficient to identify the effects of interest, and we show how the panel structure may be helpful in finding control functions. We also provide new results for the nonparametric binary dependent variable case with a lagged dependent variable.

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