Local GMM estimation of time series models with conditional moment restrictions

Nikolay Gospodinov, Taisuke Otsu

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

This paper investigates statistical properties of the local generalized method of moments (LGMM) estimator for some time series models defined by conditional moment restrictions. First, we consider Markov processes with possible conditional heteroskedasticity of unknown forms and establish the consistency, asymptotic normality, and semi-parametric efficiency of the LGMM estimator. Second, we undertake a higher-order asymptotic expansion and demonstrate that the LGMM estimator possesses some appealing bias reduction properties for positively autocorrelated processes. Our analysis of the asymptotic expansion of the LGMM estimator reveals an interesting contrast with the OLS estimator that helps to shed light on the nature of the bias correction performed by the LGMM estimator. The practical importance of these findings is evaluated in terms of a bond and option pricing exercise based on a diffusion model for spot interest rate.

Original languageEnglish
Pages (from-to)476-490
Number of pages15
JournalJournal of Econometrics
Volume170
Issue number2
DOIs
Publication statusPublished - 2012 Oct 1
Externally publishedYes

Keywords

  • Conditional heteroskedasticity
  • Conditional moment restriction
  • Higher-order expansion
  • Local GMM

ASJC Scopus subject areas

  • Economics and Econometrics

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