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

研究成果: Article査読

31 被引用数 (Scopus)

抄録

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.

本文言語English
ページ(範囲)513-527
ページ数15
ジャーナルEconometric Theory
22
3
DOI
出版ステータスPublished - 2006 6 1
外部発表はい

ASJC Scopus subject areas

  • Social Sciences (miscellaneous)
  • Economics and Econometrics

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