Nonparametric Identification of a Binary Random Factor in Cross Section Data
Yingying Dong and
Arthur Lewbel ()
No 707, Boston College Working Papers in Economics from Boston College Department of Economics
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.
Keywords: Mixture model; Random effects; Binary; Unobserved factor; Unobserved regressor; Nonparametric identification; Deconvolution; Treatment (search for similar items in EconPapers)
JEL-codes: C25 C21 (search for similar items in EconPapers)
Date: 2009-06-16, Revised 2010-07-01
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
http://fmwww.bc.edu/EC-P/wp707.pdf main text (application/pdf)
Journal Article: Nonparametric identification of a binary random factor in cross section data (2011)
Working Paper: Nonparametric identification of a binary random factor in cross section data (2009)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:boc:bocoec:707
Access Statistics for this paper
More papers in Boston College Working Papers in Economics from Boston College Department of Economics Boston College, 140 Commonwealth Avenue, Chestnut Hill MA 02467 USA. Contact information at EDIRC.
Bibliographic data for series maintained by Christopher F Baum ().