Testing for Structural Change: Evaluation of the Current Methodologies, a Misspeciâ¦cation Testing Perspective and Applications

The unit root revolution in time series modeling has created substantial interest in non-stationarity and its implications for empirical modeling. Beyond the original interest in trend vs.di¤erence non-stationarity, there has been renewed interest in testing and modeling structuralbreaks. The focus of my dissertation is on testing for departures from stationarity in a broaderframework where unit root, mean trends and structural break non-stationarity constitute onlya small subset of the possible forms of non-stationarity. In the â¦rst chapter the most populartesting procedures for the assumption, in view of the fact that general forms of non-stationarityrender each observation unique, I develop a testing procedure using a resampling scheme whichis based on a Maximum Entropy replication algorithm. The proposed misspeciâ¦cation testingprocedure relies on resampling techniques to enhance the informational content of the observeddata in an attempt to capture heterogeneity âlocallyâ using rolling window estimators of theprimary moments of the stochastic process. This provides an e¤ective way to enhance thesample information in order to assess the presence of departures from stationarity. Dependingon the sample size, the method utilizes overlapping or non-overlapping window estimates. Thee¤ectiveness of the testing procedure is assessed using extensive Monte Carlo simulations. Theuse of rolling non-overlapping windows improves the method by improving both the size andpower of the test. In particular, the new test has empirical size very close to the nominal andvery high power for a variety of departures from stationarity. The proposed procedure is thenapplied on seven macroeconomic series in the fourth chapter. Finally, the optimal choice oforthogonal polynomials, for hypothesis testing, is investigated in the last chapter.