Bootstrapping structural change tests

Otilia Boldea, Adriana Cornea-Madeira, Alastair R. Hall

Research output: Contribution to journalArticleScientificpeer-review


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.
Original languageEnglish
Pages (from-to)359-397
JournalJournal of Econometrics
Issue number2
Publication statusPublished - Dec 2019


  • multiple break points
  • instrumental variables estimation
  • two-stage least squares
  • wild bootstrap
  • recursive bootstrap
  • fixed-regressor bootstrap
  • heteroskedasticity


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