Detecting Structural Change when the Change Point is Unknown.

Student Name: Sidika Basci

There are various tests which are used to detect structural changes when the change point is unknown, like CUSUM, CUSUM of Squares, Fluctuation Test etc. 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 heteroscedasticity. For this aim, we follow a Bayesian aproach. We also present some Monte Carlo study which finds that the Bayesian test is superior to the other tests in terms of power.