Implementing Maximum Likelihood Estimation of Empirical Matching Models
- Resource Type
- Authors
- Xing Zhang; Yu-Wei Hsieh; Baiyu Dong
- Source
- Computational Economics. 59:1-32
- Subject
- Matching (statistics)
Mathematical optimization
Computer science
Maximum likelihood
Economics, Econometrics and Finance (miscellaneous)
Convergence (routing)
Structural estimation
Mathematical programming with equilibrium constraints
Computer Science Applications
Separable space
- Language
- ISSN
- 1572-9974
0927-7099
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.