A Matching Estimator Based on a Bilevel Optimization Problem

Juan Díaz, Tomás Rau and Jorge Rivera ()
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Juan Díaz: Universidad de Chile
Jorge Rivera: Universidad de Chile

The Review of Economics and Statistics, 2015, vol. 97, issue 4, 803-812

Abstract: This paper proposes a novel matching estimator where neighbors used and weights are endogenously determined by optimizing a covariate balancing criterion. The estimator is based on finding, for each unit that needs to be matched, sets of observations such that a convex combination of them has the same covariate values as the unit needing matching or with minimized distance between them. We implement the proposed estimator with data from the National Supported Work Demonstration, finding outstanding performance in terms of covariate balance. Monte Carlo evidence shows that our estimator performs well in designs previously used in the literature.

Keywords: Matching estimator; covariate balance; non-parametric methods; bi-level optimization (search for similar items in EconPapers)
JEL-codes: C01 C14 C61 (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (11)

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