What causes the forecasting failure of Markov-switching models ? A Monte Carlo study.

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;

What causes the forecasting failure of Markov-Switching models? A Monte Carlo study

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.Forecasting, Regime Shifts, Markov-Switching.

The European Way Out of Recessions

This paper proposes a two-regime Bounce-Back Function augmented Self-Exciting Threshold AutoRegression (SETAR) which allows for various shapes of recoveries from the recession regime. It relies on the bounce-back effects first analyzed in a Markov-Switching setup by Kim, Morley and Piger [2005] and recently extended by Bec, Bouabdallah and Ferrara [2011a]. This approach is then applied to post-1973 quarterly growth rates of French, German, Italian, Spanish and Euro area real GDPs. Both the linear autoregression and the standard SETAR without bounce-back effect null hypotheses are strongly rejected against the Bounce-Back augmented SETAR alternative in all cases but Italy. The relevance of our proposed model is further assessed by the comparison of its short-term forecasting performances with the ones obtained from a linear autoregression and a standard SETAR. It turns out that the bounce-back models one-step ahead forecasts generally outperform the other ones, and particularly so during the last recovery period in 2009Q3-2010Q4.Threshold autoregression, bounce-back effects, asymmetric business cycles. JEL classification: E32, C22.

The possible shapes of recoveries in Markov-Switching models

This paper explores the various shapes the recoveries may exhibit within a Markov- Switching model. It relies on the bounce-back effects first analyzed by Kim, Morley and Piger (2005) and extends the methodology by proposing i) a more flexible bounce-back model, ii) explicit tests to select the appropriate bounce-back function, if any, and iii) a suitable measure of the permanent impact of recessions. This approach is then applied to post-WWII quarterly growth rates of US, UK and French real GDPs.Markov-Switching models; bounce-back effects; asymmetric business cycles.

On Balancing Energy Consumption in Wireless Sensor Networks

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Les variables financières permettent-elles de mieux connaître l'état de l'économie en temps réel ?

Tselikas Stélios, Bouabdallah Othman. Les variables financières permettent-elles de mieux connaître l'état de l'économie en temps réel ?. In: Économie & prévision, n°182, 2008-1. pp. 131-138

What Causes The Forecasting Failure of Markov-Switching Models? A Monte Carlo Study

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.Forecasting, Regime shifts, Markov Switching,

Resolving the Unfairness Limitations of the IEEE 802.11 DCF

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