Inferences from Parametric and Non-Parametric Covariance Matrix Estimation Procedures
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 DepaDownload Info
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Paper provided by National Bureau of Economic Research, Inc in its series NBER Technical Working Papers with number 0195.Length:
Date of creation: May 1996
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Handle: RePEc:nbr:nberte:0195
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Keywords:Other versions of this item:
- Wouter J. den Haan & Andrew T. Levin, 1995. "Inferences from parametric and non-parametric covariance matrix estimation procedures," International Finance Discussion Papers 504, Board of Governors of the Federal Reserve System (U.S.).
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
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Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.Cited by:
- Chistiano, Lawrence J & den Haan, Wouter J, 1996.
"Small-Sample Properties of GMM for Business-Cycle Analysis,"
Journal of Business & Economic Statistics,
American Statistical Association, vol. 14(3), pages 309-27, July.
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- Lawrence J. Christiano & Wouter Den Haan, 1995. "Small sample properties of GMM for business cycle analysis," Staff Report 199, Federal Reserve Bank of Minneapolis.
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"Data-Driven Nonparametric Spectral Density Estimators For Economic Time Series: A Monte Carlo Study,"
Econometric Reviews,
Taylor and Francis Journals, vol. 21(4), pages 449-476.
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- Wouter J. Den Haan & Andrew T. Levin, 1996.
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