Forecasting with Structural Change
by A. Thavaneswaran and M. Ghahramani.
Many analyses of time series
forecasting have been based on the assumptions of a constant,
time-invariant, data generating process, that is stationary (no
structural change), and coincides with the time series model used.
A simple example is when the process has undergone a regime shift
(structural change), forecasts based on past information need not
be unbiased despite being the previous conditional expectation. In
this paper we study improved estimates and forecasts for time
with structural change in the mean as well as volatility.
Structural Change, GARCH, Improved Estimate/Forecast
A. Thavaneswaran, email@example.com
M. Ghahramani, firstname.lastname@example.org
READING THE ARTICLE: You can read the article in
portable document (.pdf) format (133748 bytes.)
NOTE: The content of this article is the intellectual property of the authors, who retains all rights to future publication.
This page has been accessed 2847 times since July 24, 2006.
Return to the Home Page.