Generalized empirical likelihood inference for nonlinear and time series models under weak identification

Research output: Contribution to journalArticlepeer-review

33 Citations (Scopus)

Abstract

This paper studies robust inference methods for nonlinear moment restriction models with weakly identified parameters in time series contexts. Our methods are based on generalized empirical likelihood with kernel smoothing. The proposed test statistics, which follow the standard χ 2 limiting distributions, are robust to weak identification and dependent data.

Original languageEnglish
Pages (from-to)513-527
Number of pages15
JournalEconometric Theory
Volume22
Issue number3
DOIs
Publication statusPublished - 2006 Jun 1
Externally publishedYes

ASJC Scopus subject areas

  • Social Sciences (miscellaneous)
  • Economics and Econometrics

Fingerprint Dive into the research topics of 'Generalized empirical likelihood inference for nonlinear and time series models under weak identification'. Together they form a unique fingerprint.

Cite this