Generalized empirical likelihood estimators and tests under partial, weak, and strong identification
UNSPECIFIED. (2005) Generalized empirical likelihood estimators and tests under partial, weak, and strong identification. ECONOMETRIC THEORY, 21 (4). pp. 667-709. ISSN 0266-4666Full text not available from this repository.
Official URL: http://dx.doi.org/10.1017/S0266466605050371
The purpose of this paper is to describe the performance of generalized empirical likelihood (GEL) methods for time series instrumental variable models specified by nonlinear moment restrictions as in Stock and Wright (2000, Econometrica 68, 1055-1096) when identification may be weak. The paper makes two main contributions. First, we show that all GEL estimators are first-order equivalent under weak identification. The GEL estimator under weak identification is inconsistent and has a nonstandard asymptotic distribution. Second, the paper proposes new GEL test statistics, which have chi-square asymptotic null distributions independent of the strength or weakness of identification. Consequently, unlike those for Wald and likelihood ratio statistics, the size of tests formed from these statistics is not distorted by the strength or weakness of identification. Modified versions of the statistics are presented for tests of hypotheses on parameter subvectors when the parameters not under test are strongly identified. Monte Carlo results for the linear instrumental variable regression model suggest that tests based on these statistics have very good size properties even in the presence of conditional.
|Item Type:||Journal Article|
|Subjects:||H Social Sciences > HC Economic History and Conditions
Q Science > QA Mathematics
H Social Sciences
|Journal or Publication Title:||ECONOMETRIC THEORY|
|Publisher:||CAMBRIDGE UNIV PRESS|
|Official Date:||August 2005|
|Number of Pages:||43|
|Page Range:||pp. 667-709|
Actions (login required)