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Monte Carlo Inference on Two-Sided Matching Models

Taehoon Kim (), Jacob Schwartz, Kyungchul Song () and Yoon-Jae Whang ()
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Taehoon Kim: Department of Economics, Harvard University, Cambridge, MA 02138, USA
Kyungchul Song: Vancouver School of Economics, University of British Columbia, Vancouver, BC V6T 1Z4, Canada

Econometrics, 2019, vol. 7, issue 1, 1-15

Abstract: This paper considers two-sided matching models with nontransferable utilities, with one side having homogeneous preferences over the other side. When one observes only one or several large matchings, despite the large number of agents involved, asymptotic inference is difficult because the observed matching involves the preferences of all the agents on both sides in a complex way, and creates a complicated form of cross-sectional dependence across observed matches. When we assume that the observed matching is a consequence of a stable matching mechanism with homogeneous preferences on one side, and the preferences are drawn from a parametric distribution conditional on observables, the large observed matching follows a parametric distribution. This paper shows in such a situation how the method of Monte Carlo inference can be a viable option. Being a finite sample inference method, it does not require independence or local dependence among the observations which are often used to obtain asymptotic validity. Results from a Monte Carlo simulation study are presented and discussed.

Keywords: two-sided matching; monte carlo inference; one-side homogeneous preferences; serial dictatorship mechanism (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2019
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