Approximate p-Values of Certain Tests Involving Hypotheses About Multiple Breaks
Alastair Hall and
Sakkas Nikolaos
Additional contact information
Sakkas Nikolaos: Department of Economics, University of Bath, UK
Journal of Econometric Methods, 2013, vol. 2, issue 1, 53-67
Abstract:
We provide formulae for calculating approximate p-values for the non-standard asymptotic null distributions of a variety of tests used for detecting multiple structural change in a wide range of models. Our approximations are based on simulated quantiles obtained from 100,000 replications, and the latter are more accurate than the quantiles reported in the literature by increasing the number of replications by a factor of 10. The p-value response surfaces are approximated using a parametric method proposed by Hansen and their use is illustrated with an example. Using our p-value response surfaces, it is shown that the use of Bai and Perron’s response surfaces for the critical values of these tests can lead to misleading inferences, and thus should be used with extreme caution.
Keywords: least squares; regression models; tests of parameter variation (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)
Downloads: (external link)
https://doi.org/10.1515/jem-2012-0014 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.
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:bpj:jecome:v:2:y:2013:i:1:p:53-67:n:5
Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/jem/html
DOI: 10.1515/jem-2012-0014
Access Statistics for this article
Journal of Econometric Methods is currently edited by Tong Li and Zhongjun Qu
More articles in Journal of Econometric Methods from De Gruyter
Bibliographic data for series maintained by Peter Golla ().