Two competitive models and their identification problem: The ESTAR and TSTAR model
Determining good parameter estimates in ESTAR models is known to be diffcult. We show that the phenomena of getting strongly biased estimators is a consequence of the so-called identifcation problem, the problem of properly distinguishing the transition function in relation to extreme parameter combinations. This happens in particular for either very small or very large values of the error term variance. Furthermore, we introduce a new alternative model -the TSTAR model- which has similar properties as the ESTAR model but reduces the effects of the identifcation problem. We also derive a linearity and a unit root test for this model.
|Date of creation:||May 2011|
|Contact details of provider:|| Postal: Koenigsworther Platz 1, D-30167 Hannover|
Phone: (0511) 762-5350
Fax: (0511) 762-5665
Web page: http://www.wiwi.uni-hannover.de
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:han:dpaper:dp-474. 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: (Heidrich, Christian)
If references are entirely missing, you can add them using this form.