Inferences from Parametric and Non-Parametric Covariance Matrix Estimation Procedures

Wouter J. Den Haan and Andrew Theo Levin ()
Authors registered in the RePEc Author Service: Wouter Denhaan ()

No 195, NBER Technical Working Papers from National Bureau of Economic Research, Inc

Abstract: In this paper, we propose a parametric spectral estimation procedure for constructing heteroskedasticity and autocorrelation consistent (HAC) covariance matrices. We establish the consistency of this procedure under very general conditions similar to those considered in previous research, and we demonstrate that the parametric estimator converges at a faster rate than the kernel-based estimators proposed by Andrews and Monahan (1992) and Newey and West (1994). In finite samples, our Monte Carlo experiments indicate that the parametric estimator matches, and in some cases greatly exceeds, the performance of the prewhitened kernel estimator proposed by Andrews and Monahan (1992). These simulation experiments illustrate several important limitations of non-parametric HAC estimation procedures, and highlight the advantages of explicitly modeling the temporal properties of the error terms. Wouter J. den Haan Andrew Levin Depa

JEL-codes: C12 C14 (search for similar items in EconPapers)
Date: 1996-05
Note: EFG
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Related works:
Working Paper: Inferences from parametric and non-parametric covariance matrix estimation procedures (1995)
Working Paper: Inferences from Parametric and Non-Parametric Covariance Matrix Estimation Procedures (1994)
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