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Regularized LIML for many instruments
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DOI：10.1016/j.jeconom.2015.02.018.png)
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摘要
En 中文
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. (C) 2015 The Authors. Published by Elsevier B.V.
Keyword:
Heteroskedasticity
High-dimensional models
LIML
Many instruments
MSE
Regularization methods
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ECONOMETRICA
OAAI
ECONOMETRICAIF7.1
OAAI分享收藏On the asymptotic optimality of the LIML estimator with possibly many instruments关于可能具有多种工具的LIML估计量的渐近最优性
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