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

Research output: Contribution to journalArticle

29 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

Fingerprint

time series
statistics
Time series models
Generalized empirical likelihood
Inference
Weak identification
Limiting distribution
Robust inference
Test statistic
Kernel smoothing

ASJC Scopus subject areas

  • Social Sciences (miscellaneous)
  • Economics and Econometrics

Cite this

Generalized empirical likelihood inference for nonlinear and time series models under weak identification. / Otsu, Taisuke.

In: Econometric Theory, Vol. 22, No. 3, 01.06.2006, p. 513-527.

Research output: Contribution to journalArticle

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