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

Mehmet Caner, Xu Han, Yoonseok Lee

Research output: Research - peer-reviewArticle

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.

LanguageEnglish (US)
Pages1-23
Number of pages23
JournalJournal of Business and Economic Statistics
DOIs
StateAccepted/In press - Apr 25 2017

Fingerprint

Elastic Net
Moment Estimation
Generalized Method of Moments
Moment Conditions
Moment
Model
Moment conditions
Generalized method of moments
Structural Parameters
Regularization
Valid
Standards
Structural parameters
Consistent Estimation
Moment Estimator
Serial Correlation
Lasso
Selection Model
Parameter Selection
Panel Data

ASJC Scopus subject areas

  • Statistics and Probability
  • Social Sciences (miscellaneous)
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty

Cite this

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