Bootstrapping Structural Change Tests

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JournalJournal of Econometrics
DateAccepted/In press - 17 May 2019
DateE-pub ahead of print (current) - 26 Jul 2019
Number of pages39
Early online date26/07/19
Original languageEnglish

Abstract

This paper demonstrates the asymptotic validity of methods based on the wild recursive and wild fixed bootstraps for testing hypotheses about discrete parameter change in linear models estimated via Two Stage Least Squares. The framework allows for the errors to exhibit conditional and/or unconditional
heteroscedasticity, and for the reduced form to be unstable. Simulation evidence indicates the bootstrap tests yield reliable inferences in the sample sizes often encountered in macroeconomics. If the errors exhibit unconditional heteroscedasticity 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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© 2019 The Author(s).

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