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Local GMM estimation of time series models with conditional moment restrictions

Nikolay Gospodinov () and Taisuke Otsu

Journal of Econometrics, 2012, vol. 170, issue 2, 476-490

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.

Keywords: Conditional moment restriction; Local GMM; Higher-order expansion; Conditional heteroskedasticity (search for similar items in EconPapers)
JEL-codes: C13 C22 G12 (search for similar items in EconPapers)
Date: 2012
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Working Paper: Local GMM Estimation of Time Series Models with Conditional Moment Restrictions (2008) Downloads
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:170:y:2012:i:2:p:476-490

DOI: 10.1016/j.jeconom.2012.05.017

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Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson

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