Markov models introduce persistence in the mixture distribution. In time series analysis, the mixture components relate to different persistent states characterizing the state-specific time series process. Model specification is discussed in a general form. Emphasis is put on the functional form...

Sequential Monte Carlo (SMC) methods are widely used for non-linear filtering purposes. However, the SMC scope encompasses wider applications such as estimating static model parameters so much that it is becoming a serious alternative to Markov-Chain Monte-Carlo (MCMC) methods. Not only do SMC...

Wir konstruieren ein neues Modell unbeobachteter Komponenten mit Markov-Switching zur Analyse von Hysterese-Effekten, also der Verfestigung ursprünglich zyklischer Fluktuationen. Das Modell kombiniert die Bestandteile einer Trend-Zyklus Zerlegung, der Identifikation von gegenseitigen...