Bootstrapping Structural Change Tests
Otilia Boldea (),
Adriana Cornea-Madeira () and
Alastair R. Hall
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Alastair R. Hall: University of Manchester
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This paper analyses the use of bootstrap methods to test for parameter change in linear models estimated via Two Stage Least Squares (2SLS). Two types of test are considered: one where the null hypothesis is of no change and the alternative hypothesis involves discrete change at k unknown break-points in the sample; and a second test where the null hypothesis is that there is discrete parameter change at l break-points in the sample against an alternative in which the parameters change at l + 1 break-points. In both cases, we consider inferences based on a sup-Wald-type statistic using either the wild recursive bootstrap or the wild fixed bootstrap. We establish the asymptotic validity of these bootstrap tests under a set of general conditions that allow the errors to exhibit conditional and/or unconditional heteroskedasticity, and report results from a simulation study that indicate the tests yield reliable inferences in the sample sizes often encountered in macroeconomics. The analysis covers the cases where the first-stage estimation of 2SLS involves a model whose parameters are either constant or themselves subject to discrete parameter change. If the errors exhibit unconditional heteroskedasticity and/or the reduced form is unstable then the bootstrap methods are particularly attractive because the limiting distributions of the test statistics are not pivotal.
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Published in Journal of Econometrics, 2019
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http://arxiv.org/pdf/1811.04125 Latest version (application/pdf)
Journal Article: Bootstrapping structural change tests (2019)
Working Paper: Bootstrapping Structural Change Tests (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1811.04125
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