Sample selection and information-theoretic alternatives to GMM

Aviv Nevo*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Information-theoretic alternatives to general method of moments (GMM) use over-identifying moments to estimate the data-generating distribution jointly with the parameters of interest. This paper demonstrates how these estimates can be interpreted when the sample is not a random draw from the population of interest. I make explicit the selection probability implied by the empirical likelihood and exponential tilting estimators, two commonly used estimators in this class. In addition, I propose an alternative estimator that corresponds to a logisitic selection model. The small sample properties of the estimators are demonstrated with a Monte Carlo experiment.

Original languageEnglish (US)
Pages (from-to)149-157
Number of pages9
JournalJournal of Econometrics
Volume107
Issue number1-2
DOIs
StatePublished - Mar 1 2002

Keywords

  • Exponential tilting
  • Information theory
  • Maximum entropy
  • Sample selection

ASJC Scopus subject areas

  • Economics and Econometrics
  • Finance
  • Statistics and Probability

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