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Econometrics 2019, 7(1), 14; https://doi.org/10.3390/econometrics7010014

Indirect Inference: Which Moments to Match?

1
Department of Econometrics and Business Statistics, Monash University, Melbourne 3800, Australia
2
Department of Economics, University of Warwick, Coventry CV4 7AL, UK
*
Author to whom correspondence should be addressed.
We thank Geert Dhaene for helpful comments and discussions.
Received: 19 December 2018 / Revised: 17 February 2019 / Accepted: 7 March 2019 / Published: 19 March 2019
(This article belongs to the Special Issue Resampling Methods in Econometrics)
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PDF [308 KB, uploaded 21 March 2019]

Abstract

The standard approach to indirect inference estimation considers that the auxiliary parameters, which carry the identifying information about the structural parameters of interest, are obtained from some recently identified vector of estimating equations. In contrast to this standard interpretation, we demonstrate that the case of overidentified auxiliary parameters is both possible, and, indeed, more commonly encountered than one may initially realize. We then revisit the “moment matching” and “parameter matching” versions of indirect inference in this context and devise efficient estimation strategies in this more general framework. Perhaps surprisingly, we demonstrate that if one were to consider the naive choice of an efficient Generalized Method of Moments (GMM)-based estimator for the auxiliary parameters, the resulting indirect inference estimators would be inefficient. In this general context, we demonstrate that efficient indirect inference estimation actually requires a two-step estimation procedure, whereby the goal of the first step is to obtain an efficient version of the auxiliary model. These two-step estimators are presented both within the context of moment matching and parameter matching. View Full-Text
Keywords: indirect inference; auxiliary models; overidentification indirect inference; auxiliary models; overidentification
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Frazier, D.T.; Renault, E. Indirect Inference: Which Moments to Match? Econometrics 2019, 7, 14.

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