Maximum likelihood estimation and uniform inference with sporadic identification failure

This paper analyzes the properties of a class of estimators, tests, and confidence sets (CSs) when the parameters are not identified in parts of the parameter space. Specifically, we consider estimator criterion functions that are sample averages and are smooth functions of a parameter θ. This includes log likelihood, quasi-log likelihood, and least squares criterion functions.

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Year of publication:	2013
Authors:	Andrews, Donald W.K. ; Cheng, Xu
Published in:	Journal of Econometrics. - Elsevier, ISSN 0304-4076. - Vol. 173.2013, 1, p. 36-56
Publisher:	Elsevier
Subject:	Asymptotic size \| Binary choice \| Confidence set \| Estimator \| Identification \| Likelihood \| Nonlinear models \| Test \| Smooth transition threshold autoregression \| Weak identification

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Type of publication:	Article
Classification:	C12 - Hypothesis Testing ; C15 - Statistical Simulation Methods; Monte Carlo Methods
Source:	RePEc - Research Papers in Economics

Persistent link: https://ebvufind01.dmz1.zbw.eu/10011052306