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Title:Nonparametric identification of a binary random factor in cross section data PDF Logo
Authors:Dong, Yingying
Lewbel, Arthur
Issue Date:2009
Series/Report no.:cemmap working paper CWP16/09
Abstract: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.
Subjects:Random Effects
Unobserved Factor
Unobserved Regressor
Income distribution
Income Convergence
Nonparametric identification
Nonparametric Deconvolution
Persistent Identifier of the first edition:doi:10.1920/wp.cem.2009.1609
Document Type:Working Paper
Appears in Collections:cemmap working papers, Centre for Microdata Methods and Practice, Institute for Fiscal Studies (IFS)

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