Regime switching model: real or spurious long memory?
Résumé
In this paper, we analyze the possible confusion in terms of long memory behavior of the autocorrelation function of a Markov switching model. Such a model is known to have a short memory behavior. Analyzing the value of sum of the transition probabilities and the number of switches inside such a model, we show their impact to create long memory. The ability of the true Markov switching model to predict is compared with the forecasts obtained from a long memory process adjusted on data derived from the former model. It is shown that, in certain cases, this spurious long memory behavior can be benefit to get better forecasts.
Dans ce papier, on étudie la fonction d'autocorrélation d'un processus à changement de régimes. On étudie les prévisions obtenues à partir du processus à changements de régimes et d'un processus longue mémoire. On montre aussi que la détection de mémoire longue est liée au nombre de changements d'états.
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