EconStor >
Institute for Fiscal Studies (IFS), London >
cemmap working papers, Centre for Microdata Methods and Practice, Institute for Fiscal Studies (IFS) >

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10419/64730
  
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
Binary
Unobserved Factor
Unobserved Regressor
Income distribution
Income Convergence
Nonparametric identification
Nonparametric Deconvolution
JEL:C35
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)

Files in This Item:
File Description SizeFormat
604480784.pdf390.5 kBAdobe PDF
No. of Downloads: Counter Stats
Download bibliographical data as: BibTeX
Share on:http://hdl.handle.net/10419/64730

Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.