Bootstrap Methods for Inference in a SUR model with Autocorrelated Disturbances

Clarisse Messemer and Richard Parks

No UWEC-2004-24, Working Papers from University of Washington, Department of Economics

Abstract: Although the Parks (1967) estimator for a SUR model with AR disturbances is efficient both asymptotically and in small samples, Kmenta and Gilbert (1970) and more recently Beck and Katz (1995) note that estimated standard errors tend to be biased downward as compared with the true variability of the estimates. This bias leads to tests that show over-rejection and to confidence intervals that are too small. We suggest bootstrapping the tests to correct this inference problem. After illustrating the over rejection associated with the estimated asymptotic standard errors, we develop a bootstrap approach to inference for this model, illustrate its use, and show using Monte Carlo methods that the bootstrap gives rejection probabilities close to the nominal level chosen by the researcher.

Date: 2004-10
References: Add references at CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.econ.washington.edu/user/rwparks/MessemerParks1018.pdf (application/pdf)
Our link check indicates that this URL is bad, the error code is: 404 Not Found (http://www.econ.washington.edu/user/rwparks/MessemerParks1018.pdf [301 Moved Permanently]--> https://www.econ.washington.edu/user/rwparks/MessemerParks1018.pdf [302 Found]--> https://econ.washington.edu/user/rwparks/MessemerParks1018.pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:udb:wpaper:uwec-2004-24

Access Statistics for this paper

More papers in Working Papers from University of Washington, Department of Economics Contact information at EDIRC.
Bibliographic data for series maintained by Michael Goldblatt ().