@techreport{1d969dc0891342df89c74e189eec8aae,

title = "Bootstrapping Structural Change Tests",

abstract = "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-W ald-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.",

keywords = "multiple break points, instrumental variables estimation, two-stage least squares, wild bootstrap, recursive bootstrap, fixed-regressor bootstrap, heteroskedasticity",

author = "Otilia Boldea and Adriana Cornea-Madeira and Hall, {Alastair R.}",

year = "2018",

month = nov,

day = "13",

language = "English",

series = "ArXiv",

publisher = "Cornell University Library",

type = "WorkingPaper",

institution = "Cornell University Library",

}