eprintid: 16627
rev_number: 13
eprint_status: archive
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dir: disk0/00/01/66/27
datestamp: 2015-03-16 14:52:08
lastmod: 2021-04-02 15:49:32
status_changed: 2016-09-14 12:54:11
type: article
metadata_visibility: show
creators_name: Antoine, Bertille
creators_name: Lavergne, Pascal
creators_idrefppn: 177855533
creators_affiliation: Toulouse School of Economics
title: Conditional moments models under semi-strong identification
ispublished: pub
subjects: subjects_ECO
abstract: We consider conditional moment models under semi-strong identification. Identification strength is directly defined through the conditional moments that flatten as the sample size increases. Our new minimum distance estimator is consistent, asymptotically normal, robust to semi-strong identification, and does not rely on the choice of a user-chosen parameter, such as the number of instruments or some smoothing parameter. Heteroskedasticity-robust inference is possible through Wald testing without prior knowledge of the identification pattern. Simulations show that our estimator is competitive with alternative estimators based on many instruments, being well-centered with better coverage rates for confidence intervals.
date: 2014-09
date_type: published
publisher: Elsevier
official_url: http://tse-fr.eu/pub/28781
faculty: tse
divisions: tse
keywords: Identification
keywords: Conditional moments
keywords: Minimum distance estimation
language: en
has_fulltext: FALSE
view_date_year: 2014
full_text_status: none
publication: Journal of Econometrics
volume: vol. 182
number: n° 3
pagerange: 59-69
refereed: TRUE
issn: 0304-4076
oai_identifier: oai:tse-fr.eu:28781
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oai_lastmod: 2016-07-18T11:37:24Z
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oai_set: ut1c
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citation:   Antoine, Bertille and Lavergne, Pascal <https://www.idref.fr/177855533>  (2014) Conditional moments models under semi-strong identification.    Journal of Econometrics, vol. 182 (n° 3).  pp. 59-69.