Parametric and Semiparametric Efficient Tests for Parameter Instability
This paper proposes asymptotically point optimal tests for parameter instability under the feasible circumstance that the researcher has little information about the unstable parameter process and the error distribution. The shape of the unstable parameter process is not identified but is asymptotically described by the Winer process, which is weak enough to cover a wide range of structural breaks and time varying parameter processes. I first derive a test under known error distribution, and show that the test is asymptotically equivalent to likelihood ratio tests for correctly identified unstable parameter processes under suitable conditions. The test is then extended to semiparametric models in which the underlying distribution is unknown but treated as an infinite dimensional nuisance parameter. An adaptive test is shown to be attainable without further restrictive conditions on the error distribution, which implies that the semiparametric power envelope is asymptotically equivalent to that of parametric models.
|Date of creation:||Oct 2008|
|Date of revision:||Aug 2009|
|Contact details of provider:|| Postal: University of Connecticut 365 Fairfield Way, Unit 1063 Storrs, CT 06269-1063|
Phone: (860) 486-4889
Fax: (860) 486-4463
Web page: http://www.econ.uconn.edu/
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:uct:uconnp:2008-40. 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: (Mark McConnel)
If references are entirely missing, you can add them using this form.