Bootstrapping Structural Change Tests

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.