Automatic Lag Selection in Covariance Matrix Estimation
We 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, we prove that our procedure is asymptotically equivalent to one that is optimal under a mean squared error loss function. Monte Carlo simulations suggest that our procedure performs tolerably well, although it does result in size distortions.
|Date of creation:||Feb 1995|
|Date of revision:|
|Publication status:||published as Review of Economic Studies, 1994, 61, pp 631-653|
|Contact details of provider:|| Postal: |
Web page: http://www.nber.org
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:nbr:nberte:0144. 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: ()
If references are entirely missing, you can add them using this form.