NDLI: Bootstrap methods for single structural change tests: power versus corrected size and empirical illustration

Content Provider	Springer Nature Link
Author	Jouini, Jamel
Copyright Year	2008
Abstract	This paper assesses the performance of tests for a single structural change at unknown date when regressors are stationary, trending and when they have a break in mean. Size and power of the test procedures are compared in a simulation setup particularly aimed at autoregressive models using their limiting distribution and some bootstrap approximations. The comparisons are performed using graphical methods, namely P value discrepancy plots and size–power curves. The simulation study gives some interesting insights to the test procedures. Indeed, it documents that tests based on the conventional asymptotic distribution are oversized in small samples. The size correction is achieved by some bootstrap methods which appear to possess reasonable size properties. For the power study, the proposed bootstrap method improves on the asymptotic approximations of some tests for heteroskedastic regression errors especially when there is a mean-shift in the regressors. This result has not been found for the case of i.i.d. errors where the bootstrap tests have the same power properties as the tests based on the asymptotic approximations. We finally study the relationship between two monthly US interest rates. The results show that such relationship has been altered by a regime-shift located in May 1981.
Ending Page	109
Page Count	25
Starting Page	85
File Format	PDF
ISSN	09325026
e-ISSN	16139798
Journal	Statistical Papers
Issue Number	1
Volume Number	51
Language	English
Publisher	Springer-Verlag
Publisher Date	2008-03-06
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Statistics for Business/Economics/Mathematical Finance/Insurance Bootstrap techniques Operations Research/Decision Theory Graphical methods Economic Theory Probability Theory and Stochastic Processes Break date
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Statistics, Probability and Uncertainty

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