  Markov Switching models have had increasing success in time series analysis
due to their ability to capture the existence of unobserved discrete states in
the dynamics of the variables under study. This result is generally obtained
thanks to the inference on states derived from the so--called Hamilton filter.
One of the open problems in this framework is the identification of the number
of states, generally fixed a priori; it is in fact impossible to apply
classical tests due to the problem of the nuisance parameters present only
under the alternative hypothesis. In this work we show, by Monte Carlo
simulations, that fuzzy clustering is able to reproduce the parametric state
inference derived from the Hamilton filter and that the typical indices used in
clustering to determine the number of groups can be used to identify the number
of states in this framework. The procedure is very simple to apply, considering
that it is performed (in a nonparametric way) independently of the data
generation process and that the indicators we use are present in most
statistical packages. A final application on real data completes the analysis.
