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Monte Carlo Inference on Two-Sided Matching Models

Author

Listed:
  • Taehoon Kim

    (Department of Economics, Harvard University, Cambridge, MA 02138, USA)

  • Jacob Schwartz

    (Department of Economics, University of Haifa, Haifa 3498838, Israel)

  • Kyungchul Song

    (Vancouver School of Economics, University of British Columbia, Vancouver, BC V6T 1Z4, Canada)

  • Yoon-Jae Whang

    (Department of Economics, Seoul National University, Seoul 08826, Korea)

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.

Suggested Citation

  • Taehoon Kim & Jacob Schwartz & Kyungchul Song & Yoon-Jae Whang, 2019. "Monte Carlo Inference on Two-Sided Matching Models," Econometrics, MDPI, vol. 7(1), pages 1-15, March.
    • Handle: RePEc:gam:jecnmx:v:7:y:2019:i:1:p:16-:d:217155
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    References listed on IDEAS

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    Cited by:

    1. Schwartz, Jacob & Song, Kyungchul, 2024. "The law of large numbers for large stable matchings," Journal of Econometrics, Elsevier, vol. 241(1).

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