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Kernel-weighted GMM estimators for linear time series models

  • Kuersteiner, Guido M.

This paper analyzes the higher-order asymptotic properties of generalized method of moments (GMM) estimators for linear time series models using many lags as instruments. A data-dependent moment selection method based on minimizing the approximate mean squared error is developed. In addition, a new version of the GMM estimator based on kernel-weighted moment conditions is proposed. It is shown that kernel-weighted GMM estimators can reduce the asymptotic bias compared to standard GMM estimators. Kernel weighting also helps to simplify the problem of selecting the optimal number of instruments. A feasible procedure similar to optimal bandwidth selection is proposed for the kernel-weighted GMM estimator.

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Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 170 (2012)
Issue (Month): 2 ()
Pages: 399-421

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Handle: RePEc:eee:econom:v:170:y:2012:i:2:p:399-421
Contact details of provider: Web page: http://www.elsevier.com/locate/jeconom

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