This paper explores the forecasting abilities of Markov-Switching models. Although MS models generally display a superior in-sample ﬁt relative to linear models, the gain in prediction remains small. We conﬁrm this result using simulated data for a wide range of speciﬁcations. In order to explain this poor performance, we use a forecasting error decomposition. We identify four components and derive their analytical expressions in diﬀerent MS speciﬁcations. The relative contribution of each source is assessed through Monte Carlo simulations. We ﬁnd that the main source of error is due to the misclassiﬁcation of future regimes.Markov Switching; Regime Shifts; Forecasting;
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