Implementing Maximum Likelihood Estimation of Empirical Matching Models : MPEC versus NFXP

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

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Year of publication:	2020
Authors:	Dong, Baiyu
Other Persons:	Hsieh, Yu-Wei (contributor) ; Zhang, Xing (contributor)
Publisher:	[2020]: [S.l.] : SSRN
Subject:	Maximum-Likelihood-Schätzung \| Maximum likelihood estimation \| Matching \| Schätztheorie \| Estimation theory

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