Bootstrapping Structural Change Tests

Otilia Boldea, Adriana Cornea-Madeira, Alastair Hall

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)359-397
JournalJournal of Econometrics
Issue number2
Publication statusPublished - 26 Jul 2019


  • Multiple Break Points
  • Instrumental Variables Estimation
  • Two-stage Least Squares
  • Wild bootstrap
  • Heteroskedasticity.


Dive into the research topics of 'Bootstrapping Structural Change Tests'. Together they form a unique fingerprint.

Cite this