Nonparametric Identification of a Binary Random Factor in Cross Section Data
Suppose V and U are two independent mean zero random variables, where V has an asymmetric distribution with two mass points and U has a symmetric distribution. We show that the distributions of V and U are nonparametrically identified just from observing the sum V+U, and provide a rate root n estimator. We apply these results to the world income distribution to measure the extent of convergence over time, where the values V can take on correspond to country types, i.e., wealthy versus poor countries. We also extend our results to include covariates X, showing that we can nonparametrically identify and estimate cross section regression models of the form Y=g(X,D*)+U, where D* is an unobserved binary regressor.
Random Effects, Binary, Unobserved Factor, Unobserved Regressor, Income distribution, Income Convergence, Nonparametric identiﬁcation, Nonparametric Deconvolution
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