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Multimodality in GARCH regression models
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DOI：10.1016/j.ijforecast.2008.06.002.png)
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摘要
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It is shown empirically that mixed autoregressive moving average regression models with generalized autoregressive conditional heteroskedasticity (Reg-ARMA-GARCH models) can have multimodality in the likelihood that is caused by a dummy variable in the conditinal mean. Maximum likelihood estimates at the local and global models are investigated and turn out to be qualitatively different, leading to different model-based forecast intervals. In the simpler GARCH (p,q) regression model, we derive analytical conditins for bimodality of the corresponding likelihood. In that case, the likelihood is symmertical around a local minimum. We propose a solution to avoid this bimodality. (C) International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
Keyword:
ARIMA models
dummy variable
forecasting practice
GARCH models
inflation forecasting
intervention analysis
multimodality
outliers
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