Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/129982 
Erscheinungsjahr: 
2015
Schriftenreihe/Nr.: 
School of Economics Discussion Papers No. 1515
Verlag: 
University of Kent, School of Economics, Canterbury
Zusammenfassung: 
The use of many moment conditions improves the asymptotic efficiency of the instrumental variables estimators. However, in finite samples, the inclusion of an excessive number of moments increases the bias. To solve this problem, we propose regularized versions of the limited information maximum likelihood (LIML) based on three different regularizations: Tikhonov, Landweber Fridman, and principal components. Our estimators are consistent and asymptotically normal under heteroskedastic error. Moreover, they reach the semiparametric efficiency bound assuming homoskedastic error. We show that the regularized LIML estimators possess finite moments when the sample size is large enough. The higher order expansion of the mean square error (MSE) shows the dominance of regularized LIML over regularized two-staged least squares estimators. We devise a data driven selection of the regularization parameter based on the approximate MSE. A Monte Carlo study and two empirical applications illustrate the relevance of our estimators.
Schlagwörter: 
heteroskedasticity
high-dimensional models
LIML
many instruments
MSE
regularization methods
JEL: 
C13
Dokumentart: 
Working Paper

Datei(en):
Datei
Größe
620.39 kB





Publikationen in EconStor sind urheberrechtlich geschützt.