The Finite-Sample Effects of VAR Dimensions on MLE Bias, MLE Variance and Minimum MSE Estimators: Purely Nonstationary Case
Vector autoregressions (VAR's) are an important tool in time series analysis. However, relatively little is known about the finite-sample behaviour of parameter estimators. We address this issue here, by investigating maximum likelihood estimators (MLE's) in the context of a purely nonstationary first-order VAR. Using Monte Carlo simulation and numerical optimization, we derive response surfaces for MLE bias, in terms of VAR dimensions, given correct and over-parameterization of the model. We study non-zero initial values, and show that univariate bias nonmonotonicity disappears in the multivariate case. Lastly, we examine MLE variance and the correction factors required for the MLE to attain minimum mean squared error (MSE). Contact Details (for paper requests) : Dr. Steve Lawford ECARES Universite Libre de Bruxelles 50 Avenue F. D. Roosevelt CP 114 B-1050 Brussels Belgium Fax: +32 (0)2 650 4475 e-mail: email@example.com
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:|
|Date of revision:|
|Contact details of provider:|| Postal: |
Phone: (0)1904 323776
Fax: (0)1904 323759
Web page: http://www.york.ac.uk/economics/
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:yor:yorken:02/04. 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.