Approximate Maximum Likelihood for Complex Structural Models
Abstract
Indirect Inference (II) is a popular technique for estimating complex parametric models whose likelihood function is intractable, however, the statistical efficiency of II estimation is questionable. While the efficient method of moments, Gallant and Tauchen (1996), promises efficiency, the price to pay for this efficiency is a loss of parsimony and thereby a potential lack of robustness to model misspecification. This stands in contrast to simpler II estimation strategies, which are known to display less sensitivity to model misspecification precisely due to their focus on specific elements of the underlying structural model. In this research, we propose a new simulationbased approach that maintains the parsimony of II estimation, which is often critical in empirical applications, but can also deliver estimators that are nearly as efficient as maximum likelihood. This new approach is based on using a constrained approximation to the structural model, which ensures identification and can deliver estimators that are nearly efficient. We demonstrate this approach through several examples, and show that this approach can deliver estimators that are nearly as efficient as maximum likelihood, when feasible, but can be employed in many situations where maximum likelihood is infeasible.
 Publication:

arXiv eprints
 Pub Date:
 June 2020
 arXiv:
 arXiv:2006.10245
 Bibcode:
 2020arXiv200610245C
 Keywords:

 Economics  Econometrics;
 Quantitative Finance  Statistical Finance;
 Statistics  Applications