Title page for ETD etd-04092006-225731


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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