Identication and Estimation of a Nonparametric Panel Data Model with Unobserved Heterogeneity

  • Evdokimov K
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Abstract

This paper considers a nonparametric panel data model with nonadditive unobserved heterogeneity. As in the standard linear panel data model, two types of unobservables are present in the model: individual-speci…c e¤ects and idiosyncratic disturbances. The individual-speci…c e¤ects enter the structural function nonseparably and are allowed to be correlated with the covariates in an arbitrary manner. The idiosyncratic disturbance term is additively separable from the structural function. Nonparametric identi…cation of all the structural elements of the model is established. No parametric distributional or functional form assumptions are needed for identi…cation. The identi…cation result is constructive and only requires panel data with two time periods. Thus, the model permits nonparametric distributional and counterfactual analysis of heterogeneous marginal e¤ects using short panels. The paper also develops a nonparametric estimation procedure and derives its rate of convergence. As a by-product the rates of convergence for the problem of conditional deconvolution are obtained. The proposed estimator is easy to compute and does not require numeric optimization. A Monte-Carlo study indicates that the estimator performs very well in …nite samples. ?This

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Authors

  • Kirill Evdokimov

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