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Implementing Maximum Likelihood Estimation of Empirical Matching Models

Author

Listed:
  • Baiyu Dong

    (University of Southern California)

  • Yu-Wei Hsieh

    (University of Southern California)

  • Xing Zhang

    (Microsoft Corporation)

Abstract

We propose two Mathematical Programming with Equilibrium Constraints (MPEC) formulations: the MPEC-Sparse and the MPEC-Dense to estimate a class of separable matching models. We compare MPEC with the Nested Fixed-Point (NFXP) algorithm—a well-received method in the literature of structural estimation. Using both simulated and actual data, we find that MPEC is more robust than NFXP in terms of convergence and solution quality. In terms of computing time, MPEC-Dense is 9 to 20 times faster than NFXP in simulations. For practitioners, MPEC is considerably simpler to program.

Suggested Citation

  • Baiyu Dong & Yu-Wei Hsieh & Xing Zhang, 2022. "Implementing Maximum Likelihood Estimation of Empirical Matching Models," Computational Economics, Springer;Society for Computational Economics, vol. 59(1), pages 1-32, January.
    • Handle: RePEc:kap:compec:v:59:y:2022:i:1:d:10.1007_s10614-020-10072-8
    DOI: 10.1007/s10614-020-10072-8
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    References listed on IDEAS

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    Cited by:

    1. Kuan‐Ming Chen & Yu‐Wei Hsieh & Ming‐Jen Lin, 2023. "Reducing Recommendation Inequality Via Two‐Sided Matching: A Field Experiment Of Online Dating," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(3), pages 1201-1221, August.

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