Kernel-weighted GMM estimators for linear time series models

Guido Kuersteiner

Journal of Econometrics, 2012, vol. 170, issue 2, 399-421

Abstract: 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.

Keywords: Time series; Feasible GMM; Number of instruments; Kernel weights; Higher-order MSE; Bias reduction (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:170:y:2012:i:2:p:399-421

DOI: 10.1016/j.jeconom.2012.05.013

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