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A New Approach to Unit Root Tests in Univariate Time Series Robust to Structural Changes.

作者:Seong-Tae Kim,
畢業學校:NCSU
出版單位:NCSU
核准日期:2007-01-09
類型:Electronic Thesis or Dissertation
權限:unrestricted.I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my th....

英文摘要

Using methodology in panel unit root tests we propose a new approach to univariate
unit root tests. Our method leads to an asymptotically normal distribution of the least
squares estimator and is robust to contaminated data having structural changes or outliers
while the power of the test does not drastically worsen.
The main idea is that under the assumption that the
process has a unit root we transform an AR(1) process {y_t: 1 <= t <= T} to
a double-index process {y_{ij}: 1<= i <= m, 1 <= j <= n, mn=T} in such a way that the segments are
independent for $i=1,2, ..., m.
For this transformed data, we apply the same sequential limit as in Levin and Lin (1992, 2002).
First, as n goes to infinity we obtain
asymptotic results for each i. These have the same form as in conventional univariate unit root tests.
Second, as m goes to infinity, we obtain an asymptotically normal distribution for the OLS estimator
by the Lindeberg-Feller CLT. An advantage of this technique is that an undetected break has a relatively minor
effect which, in fact, disappears as m increases. We also show that for a general ARMA (p,q) model we still
obtain the asymptotic normality of the unit root statistics under the sequential limit assumption.


committee_member - Sastry G. Pantula

committee_member - Alastair R. Hall

committee_member - Bibhuti B. Bhattacharyya

chair - David A. Dickey


 

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