The Library
Indirect inference : which moments to match?
Tools
Tools
Tools
Frazier, David T. and Renault, Eric (2019) Indirect inference : which moments to match? Econometrics, 7 (1). 14. doi:10.3390/econometrics7010014
|
PDF
WRAP-indirect-inference-which-moments-match-Renault-2019.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (308Kb) | Preview |
|
![[img]](http://wrap.warwick.ac.uk/style/images/fileicons/application_pdf.png) |
PDF
WRAP-indirect-inference-which-moments-match-Frazier-2019.pdf - Accepted Version Embargoed item. Restricted access to Repository staff only - Requires a PDF viewer. Download (585Kb) |
Official URL: https://doi.org/10.3390/econometrics7010014
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
| Item Type: | Journal Article | ||||||
|---|---|---|---|---|---|---|---|
| Subjects: | B Philosophy. Psychology. Religion > BC Logic H Social Sciences > HB Economic Theory Q Science > QA Mathematics |
||||||
| Divisions: | Faculty of Social Sciences > Economics | ||||||
| Library of Congress Subject Headings (LCSH): | Parameter estimation, Inference, Econometrics | ||||||
| Journal or Publication Title: | Econometrics | ||||||
| Publisher: | MDPI | ||||||
| ISSN: | 2225-1146 | ||||||
| Official Date: | 19 March 2019 | ||||||
| Dates: |
|
||||||
| Volume: | 7 | ||||||
| Number: | 1 | ||||||
| Article Number: | 14 | ||||||
| DOI: | 10.3390/econometrics7010014 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Publisher Statement: | This is a pre-copyedited, author-produced version of an article accepted for publication in Econometrics Journal following peer review. The version of record [insert complete citation information here] is available online at: xxxxxxx [insert URL and DOI of the article on the OUP website]. | ||||||
| Access rights to Published version: | Open Access | ||||||
| Related URLs: |
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
