A Robust Test For Autocorrelation in the Presence of Statistical Dependence
AbstractThe problem addressed in this paper is to test the null hypothesis that a time series process is uncorrelated up to lag K in the presence of statistical dependence. We propose a robust test that is asymptotically distributed as chi-square when the null is true. The test is based on a consistent estimator of the asymptotic covariance matrix of the sample autocorrelations under the null. Two consistent estimation procedures are considered. Both employ automatic data-based methods to select tuning parameters. The performance of the two variants of the robust test is compared in a Monte Carlo study.
Download InfoTo our knowledge, this item is not available for download. To find whether it is available, there are three options:
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.
Bibliographic InfoPaper provided by University of Iowa, Department of Economics in its series Working Papers with number 99-07.
Length: 29 pages
Date of creation: Jul 1999
Date of revision:
Contact details of provider:
Postal: University of Iowa, Department of Economics, Henry B. Tippie College of Business, Iowa City, Iowa 52242
Phone: (319) 335-0829
Fax: (319) 335-1956
Web page: http://tippie.uiowa.edu/economics/
More information through EDIRC
You can help add them by filling out this form.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (John Solow).
If references are entirely missing, you can add them using this form.