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Activity Number:
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150
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Type:
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Topic Contributed
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Date/Time:
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Monday, August 3, 2009 : 10:30 AM to 12:20 PM
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Sponsor:
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Business and Economic Statistics Section
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| Abstract - #304426 |
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Title:
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Local GMM Estimation of Time Series Models with Conditional Moment Restrictions
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Author(s):
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Nikolay Gospodinov*+ and Taisuke Otsu
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Companies:
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Concordia University and Yale University
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Address:
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1455 de Maisonneuve Blvd. West, Montreal, QC, H3G 1M8, Canada
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Keywords:
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Conditional moment restrictions ; Local GMM ; Higher-order expansion ; Conditional heteroskedasticity
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Abstract:
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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 semiparametric 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.
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