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11.
We consider model selection facing uncertainty over the choice of variables and the occurrence and timing of multiple location shifts. General-to-simple selection is extended by adding an impulse indicator for every observation to the set of candidate regressors: see Johansen and Nielsen (2009). We apply that approach to a fat-tailed distribution, and to processes with breaks: Monte Carlo experiments show its capability of detecting up to 20 shifts in 100 observations, while jointly selecting variables. An illustration to US real interest rates compares impulse-indicator saturation with the procedure in Bai and Perron (1998). 相似文献
12.
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 conditional mean. Maximum likelihood estimates at the local and global modes 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 conditions for bimodality of the corresponding likelihood. In that case, the likelihood is symmetrical around a local minimum. We propose a solution to avoid this bimodality. 相似文献