Monte carlo inference on two-sided matching models

Taehoon Kim, Jacob Schwartz, Kyungchul Song, Yoon Jae Whang

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)16
Issue number1
StatePublished - Mar 2019

Bibliographical note

Funding Information:
Social Sciences and Humanities Research Council in Canada.

Publisher Copyright:
© 2019 by the authors. Licensee MDPI, Basel, Switzerland.


  • Monte carlo inference
  • One-side homogeneous preferences
  • Serial dictatorship mechanism
  • Two-sided matching

ASJC Scopus subject areas

  • Economics and Econometrics


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