Forecasting with Structural Change
by A. Thavaneswaran and M. Ghahramani.
Abstract:
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
series models
with structural change in the mean as well as volatility.
Key Words:
Structural Change, GARCH, Improved Estimate/Forecast
Authors:
A. Thavaneswaran, thavane@cc.umanitoba.ca
M. Ghahramani, umghahra@cc.umanitoba.ca
Editor:
Shelton Peiris,shelton@maths.usyd.edu.au
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