Testing for Structural Breaks at Unknown Time: A Steeplechase

This paper analyzes the role of common data problems when identifying structural breaks in small samples. Most notably, we survey small sample properties of the most commonly applied endogenous break tests developed by Brown et al. (J R Stat Soc B 37:149–163, <CitationRef CitationID="CR10">1975</CitationRef>) and Zeileis (Stat Pap 45(1):123–131, <CitationRef CitationID="CR41">2004</CitationRef>), Nyblom (J Am Stat Assoc 84(405):223–230, <CitationRef CitationID="CR35">1989</CitationRef>) and Hansen (J Policy Model 14(4):517–533, <CitationRef CitationID="CR23">1992</CitationRef>), and Andrews et al. (J Econ 70(1):9–38, <CitationRef CitationID="CR4">1996</CitationRef>). Power and size properties are derived using Monte Carlo simulations. We find that the Nyblom test is on par with the commonly used F type tests in a small sample in terms of power. While the Nyblom test’s power decreases if the structural break occurs close to the margin of the sample, it proves far more robust to nonnormal distributions of the error term that are found to matter strongly in small samples although being irrelevant asymptotically for all tests that are analyzed in this paper. Copyright Springer Science+Business Media, LLC. 2013