Adaptive Elastic Net GMM Estimation With Many Invalid Moment Conditions : Simultaneous Model and Moment Selection

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

2 Scopus Citations
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Author(s)

  • Mehmet CANER
  • Xu HAN
  • Yoonseok LEE

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)24-46
Journal / PublicationJournal of Business and Economic Statistics
Volume36
Issue number1
Online published18 Dec 2015
Publication statusPublished - Jan 2018

Abstract

This article develops the adaptive elastic net generalized method of moments (GMM) estimator in large-dimensional models with potentially (locally) invalid moment conditions, where both the number of structural parameters and the number of moment conditions may increase with the sample size. The basic idea is to conduct the standard GMM estimation combined with two penalty terms: the adaptively weighted lasso shrinkage and the quadratic regularization. It is a one-step procedure of valid moment condition selection, nonzero structural parameter selection (i.e., model selection), and consistent estimation of the nonzero parameters. The procedure achieves the standard GMM efficiency bound as if we know the valid moment conditions ex ante, for which the quadratic regularization is important. We also study the tuning parameter choice, with which we show that selection consistency still holds without assuming Gaussianity. We apply the new estimation procedure to dynamic panel data models, where both the time and cross-section dimensions are large. The new estimator is robust to possible serial correlations in the regression error terms.

Research Area(s)

  • Dynamic panel, Efficiency bound, GMM, Large-dimensional models, Shrinkage, Tuning parameter choice