The Role of Ancillarity in Inference for Non-stationary Variables
Some examples of the regression method are compared with likelihood-based inference. It is shown that, although the asymptotic theory is distinctly different for ergodic and nonergodic processes, the likelihood methods lead to the result that asymptotic inference can be conducted in the same way for the two cases by appealing to classical conditioning arguments from statistics using the notion of S-ancillarity or strong exogeneity. It is pointed out that the Fisher information can be considered a measure of the conditional variance of the maximum likelihood estimator given the available information in the sample. Copyright 1995 by Royal Economic Society.
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): 105 (1995)
Issue (Month): 429 (March)
|Contact details of provider:|| Postal: 2 Dean Trench Street, Westminster, SW1P 3HE|
Phone: +44 20 3137 6301
Web page: http://www.res.org.uk/
More information through EDIRC
|Order Information:||Web: http://www.blackwellpublishers.co.uk/asp/journal.asp?ref=0013-0133|