TY - JOUR
T1 - Local GMM estimation of time series models with conditional moment restrictions
AU - Gospodinov, Nikolay
AU - Otsu, Taisuke
N1 - Funding Information:
We would like to thank the editors, a co-editor, three anonymous referees, conference participants at the 2007 CIREQ Conference on Generalized Method of Moments, the 2007 meeting of the Canadian Econometrics Study Group and the 2009 Joint Statistical Meetings for useful comments and suggestions. Financial support from FQRSC and SSHRC (NG) and the National Science Foundation under SES-0720961 (TO) is gratefully acknowledged.
PY - 2012/10
Y1 - 2012/10
N2 - 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.
AB - 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.
KW - Conditional heteroskedasticity
KW - Conditional moment restriction
KW - Higher-order expansion
KW - Local GMM
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U2 - 10.1016/j.jeconom.2012.05.017
DO - 10.1016/j.jeconom.2012.05.017
M3 - Article
AN - SCOPUS:84865339873
VL - 170
SP - 476
EP - 490
JO - Journal of Econometrics
JF - Journal of Econometrics
SN - 0304-4076
IS - 2
ER -