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What Causes The Forecasting Failure of Markov-Switching Models? A Monte Carlo Study Author info | Abstract | Publisher info | Download info | Related research | Statistics Marie Bessec (EURIsCO, University of Paris Dauphine)
Othman Bouabdallah (EUREQua, University of Paris 1 Panthéon Sorbonne)
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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 by applying several tests of forecast accuracy and encompassing robust to nested models. 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.
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Article provided by Berkeley Electronic Press in its journal Studies in Nonlinear Dynamics & Econometrics .
Volume (Year): 9 (2005)
Issue (Month): 2 ()
Pages: 1171-1171
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Keywords: Forecasting Regime shifts Markov Switching Other versions of this item:
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