Two estimators of the long-run variance
We deal with the important question of estimating the long-run variance of a stationary sequence. We derive the asymptotic properties of a generalized Newey-West type of estimator in the case of a linear I(d) process. The results show that the bias and asymptotic distribution of the generalized Newey-West estimator depend on the memory parameter d. If the series has long memory then the estimator might even have a non-Gaussian limit distribution. The optimal bandwidth parameter q minimising MSE is derived. Theoretical results explain the large bias observed in simulation studies with arbitrarily chosen q. An alternative estimator is suggested. It has an asymptotic Gaussian distribution and bias which do not depend on d. The estimator is easy to apply and can be used to construct confidence intervals. Simulations confirm the theoretical findings.
To our knowledge, this item is not available for
download. To find whether it is available, there are three
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.
|Date of creation:|
|Contact details of provider:|| Postal: Department of Economics and Related Studies, University of York, York, YO10 5DD, United Kingdom|
Phone: (0)1904 323776
Web page: https://www.york.ac.uk/economics/
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:yor:yorken:05/19. 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: (Paul Hodgson)
If references are entirely missing, you can add them using this form.