A Hidden Markov Model (HMM) is used to classify an out of sample <p> observation vector into either of two regimes. This leads to a procedure for making probability forecasts for changes of regimes in a time series, i.e. for turning points. <p> Instead o maximizing a likelihood, the model is estimated...</p></p>

We introduce a method for dynamic classification of vector time series data into different regimes. A hidden Markov regime-switching model is used in classification. Past regimes are determined in advance and characterized by first and second moments of the observation vector. In estimation and...