Detecting structural change when the change point is unknown

Abstract

There are various tests which are used to detect structural change when the change point is unknown. Among these widely used ones are Cumulated Sums (CUSUM) and CUSUM of Squares tests of Brown, Durbin and Evans (1975), Fluctuation test of Sen (1980) and Ploberger, Krämer and Kontrus (1989). More recently, Andrews (1990) suggests Sup F test and shows that it performs better than the above stated tests in terms of power. The problem with these tests is that they all assume stable variance although the regression coefficients change while moving from one regime to the other. In this thesis, we relax this assumption and suggest an alternative test which also allows heteroskedasticity. For this aim, we follow the Bayesian approach. We also present some of the Monte Carlo study results where we find that Bayesian test has superiority over the above stated tests in terms of power.