Local GMM Estimation of Time Series Models with Conditional Moment Restrictions

Nikolay Gospodinov and Taisuke Otsu (taisuke.otsu@yale.edu)
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Taisuke Otsu: Yale University, https://www.yale.edu/

No 8010, Working Papers from Concordia University, Department of Economics

Abstract: This paper investigates statistical properties of the local GMM (LGMM) estimator for some time series models defined by conditional moment restrictions. First, we consider Markov processes with possible conditional heteroskedasticity of unknown form and establish the consistency, asymptotic normality, and semi-parametric efficiency of the estimator. Second, inspired by simulation results showing that the LGMM estimator has a significantly smaller bias than the OLS estimator, we undertake a higher-order asymptotic expansion and analyze the bias properties of the LGMM estimator. The structure of the asymptotic expansion of the LGMM estimator reveals an interesting contrast with the OLS estimator that helps to explain the bias reduction in 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 restrictions; Local GMM; Higher-order expansion; Conditional heteroskedasticity (search for similar items in EconPapers)
JEL-codes: C13 C22 G12 (search for similar items in EconPapers)
Pages: 33 pages
Date: 2008-12
New Economics Papers: this item is included in nep-ecm and nep-ets
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Citations: View citations in EconPapers (2)

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Journal Article: Local GMM estimation of time series models with conditional moment restrictions (2012)
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Persistent link: https://EconPapers.repec.org/RePEc:crd:wpaper:08010

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