Inferences from Parametric and Non-Parametric Covariance Matrix Estimation Procedures / Wouter J. Den Haan, Andrew Levin.

Format:

Book

Author/Creator:

Haan, Wouter J. Den.

Contributor:

National Bureau of Economic Research.

Levin, Andrew.

Series:

Technical Working Paper Series (National Bureau of Economic Research) no. t0195.

NBER technical working paper series no. t0195

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 1996.

Summary:

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

Notes:

Print version record

May 1996.