Type of Document	Dissertation
Author	Koutris, Andreas
URN	etd-04092006-225731
Title	Testing for Structural Change: Evaluation of the Current Methodologies, a Misspeci…cation Testing Perspective and Applications
Degree	PhD
Department	Economics
Advisory Committee	Advisor Name Title Spanos, Aris Committee Chair Ashley, Richard A. Committee Member Heracleous, Maria Committee Member McGuirk, Anya M. Committee Member Yang, Dennis T. Committee Member Yau, Jeffrey Committee Member
Keywords	Maximum Entropy Bootstrap Non-Stationarity Parameter/Structural Stability
Date of Defense	2006-03-31
Availability	unrestricted
Abstract 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 structural breaks. The focus of my dissertation is on testing for departures from stationarity in a broader framework where unit root, mean trends and structural break non-stationarity constitute only a small subset of the possible forms of non-stationarity. In the …rst chapter the most popular testing procedures for the assumption, in view of the fact that general forms of non-stationarity render each observation unique, I develop a testing procedure using a resampling scheme which is based on a Maximum Entropy replication algorithm. The proposed misspeci…cation testing procedure relies on resampling techniques to enhance the informational content of the observed data in an attempt to capture heterogeneity ‘locally’ using rolling window estimators of the primary moments of the stochastic process. This provides an e¤ective way to enhance the sample information in order to assess the presence of departures from stationarity. Depending on the sample size, the method utilizes overlapping or non-overlapping window estimates. The e¤ectiveness of the testing procedure is assessed using extensive Monte Carlo simulations. The use of rolling non-overlapping windows improves the method by improving both the size and power of the test. In particular, the new test has empirical size very close to the nominal and very high power for a variety of departures from stationarity. The proposed procedure is then applied on seven macroeconomic series in the fourth chapter. Finally, the optimal choice of orthogonal polynomials, for hypothesis testing, is investigated in the last chapter.
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