Consistent Estimation of Linear Regression Models Using Matched Data

Masayuki Hirukawa and Artem Prokhorov

Working Papers from University of Sydney Business School, Discipline of Business Analytics

Abstract: Economists often use matched samples, especially when dealing with earnings data where a number of missing observations need to be imputed. In this paper, we demonstrate that the ordinary least squares estimator of the linear regression model using matched samples is inconsistent and has a nonstandard convergence rate to its probability limit. If only a few variables are used to impute the missing data, then it is possible to correct for the bias. We propose two semiparametric bias-corrected estimators and explore their asymptotic properties. The estimators have an indirect-inference interpretation and they attain the parametric convergence rate if the number of matching variables is no greater than three. Monte Carlo simulations confirm that the bias correction works very well in such cases.

Keywords: measurement error bias; matching estimation; linear regression; indirect inference; Bias correction (search for similar items in EconPapers)
Date: 2017-03-16
New Economics Papers: this item is included in nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/2123/18063

Related works:
Journal Article: Consistent estimation of linear regression models using matched data (2018)
Working Paper: Consistent Estimation of Linear Regression Models Using Matched Data (2014)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:syb:wpbsba:2123/18063

Access Statistics for this paper

More papers in Working Papers from University of Sydney Business School, Discipline of Business Analytics Contact information at EDIRC.
Bibliographic data for series maintained by Artem Prokhorov ().