Automatic Lag Selection in Covariance Matrix Estimation
The authors propose a nonparametric method for automatically selecting the number of autocovariances to use in computing a heteroskedasticity and autocorrelation consistent covariance matrix. For a given kernel for weighting the autocovariances, they prove that their procedure is asymptotically equivalent to one that is optimal under a mean-squared error loss function. Monte Carlo simulations suggest that the authors' procedure performs tolerably well, although it does result in size distortions. Copyright 1994 by The Review of Economic Studies Limited.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 61 (1994)
Issue (Month): 4 (October)
|Contact details of provider:|| Web page: http://www.blackwellpublishing.com/journal.asp?ref=0034-6527|
|Order Information:||Web: http://www.blackwellpublishing.com/subs.asp?ref=0034-6527|
When requesting a correction, please mention this item's handle: RePEc:bla:restud:v:61:y:1994:i:4:p:631-53. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Wiley-Blackwell Digital Licensing)or (Christopher F. Baum)
If references are entirely missing, you can add them using this form.