Adaptive elastic net for generalized methods of moments

Mehmet Caner, Hao Helen Zhang

Research output: Contribution to journalArticlepeer-review

36 Scopus citations


Model selection and estimation are crucial parts of econometrics. This article introduces a new technique that can simultaneously estimate and select the model in generalized method of moments (GMM) context. The GMMis particularly powerful for analyzing complex datasets such as longitudinal and panel data, and it has wide applications in econometrics. This article extends the least squares based adaptive elastic net estimator by Zou and Zhang to nonlinear equation systems with endogenous variables. The extension is not trivial and involves a new proof technique due to estimators' lack of closed-form solutions. Compared to Bridge-GMM by Caner, we allow for the number of parameters to diverge to infinity as well as collinearity among a large number of variables; also, the redundant parameters are set to zero via a data-dependent technique. Thismethod has the oracle property, meaning thatwe can estimate nonzero parameterswith their standard limit and the redundant parameters are dropped from the equations simultaneously. Numerical examples are used to illustrate the performance of the new method.

Original languageEnglish (US)
Pages (from-to)30-47
Number of pages18
JournalJournal of Business and Economic Statistics
Issue number1
StatePublished - 2014


  • GMM
  • Oracle property
  • Penalized estimators

ASJC Scopus subject areas

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


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