Implementing Maximum Likelihood Estimation of Empirical Matching Models

Baiyu Dong (), Yu-Wei Hsieh () and Xing Zhang ()
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Baiyu Dong: University of Southern California
Yu-Wei Hsieh: University of Southern California
Xing Zhang: Microsoft Corporation

Computational Economics, 2022, vol. 59, issue 1, No 5, 32 pages

Abstract: 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.

Keywords: Aggregate matching; Two-sided matching; Separable matching models; Mathematical programming with equilibrium constraints; Nested fixed-point algorithm; Constrained optimization (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10614-020-10072-8

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