Consistent Estimation of Linear Regression Models Using Matched Data

Masayuki Hirukawa and Artem Prokhorov

No 2014-03, 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 non-standard 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 semi-parametric bias-corrected estimators and explore their asymptotic properties. The estimators have an indirectinference interpretation and their convergence rates depend on the number of variables used in matching. We can attain the parametric convergence rate if that number is no greater than three. Monte Carlo simulations confirm that the bias correction works very well in such cases.

Keywords: Bias correction; differencing; indirect inference; linear regression; matching estimation; measurement error bias (search for similar items in EconPapers)
Date: 2014-09-05
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Citations: View citations in EconPapers (1)

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Journal Article: Consistent estimation of linear regression models using matched data (2018)
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