Title: Structural change and the problem of phantom break locations
Authors: Yao Rao - The University of Liverpool (United Kingdom) [presenting]
Abstract: It is well known, in structural break problems, that it is much easier to detect the existence of a break in a data set than to determine the location of such a break in the sample span. The aim is to investigate why, in the context of Gaussian linear regressions, using a decision theory framework. The nub of the problem, even for moderately sized breaks, is that the posterior probability distribution of the possible break points is usually not very informative about the true break location. Hence, even a locally optimal break location procedure, as introduced here, is ineffective. In the regression context, it turns out to be quite common, indeed the norm, for break location procedures to misidentify the true break position up to 100\% of the time. Unfortunately too, the magnitude of the difference between the miss-identified and true break locations is usually not small.