Bootstrapping structural change tests

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

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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. (C) 2019 The Author(s). Published by Elsevier B.V.

Original languageEnglish
Pages (from-to)359-397
Number of pages39
JournalJournal of Econometrics
Volume213
Issue number2
DOIs
Publication statusPublished - Dec 2019

Keywords

  • Multiple break points
  • Instrumental variables estimation
  • Two-Stage Least Squares
  • Wild bootstrap
  • Heteroskedasticity
  • WILD BOOTSTRAP
  • PARAMETER INSTABILITY
  • RESAMPLING METHODS
  • LINEAR-MODELS
  • INFERENCE
  • BREAKS
  • HETEROSKEDASTICITY
  • JACKKNIFE
  • INFLATION

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