Autoregressive Moving Average Infinite Hidden Markov-Switching Models

Luc Bauwens, Jean-François Carpantier and Arnaud Dufays

Journal of Business & Economic Statistics, 2017, vol. 35, issue 2, 162-182

Abstract: Markov-switching models are usually specified under the assumption that all the parameters change when a regime switch occurs. Relaxing this hypothesis and being able to detect which parameters evolve over time is relevant for interpreting the changes in the dynamics of the series, for specifying models parsimoniously, and may be helpful in forecasting. We propose the class of sticky infinite hidden Markov-switching autoregressive moving average models, in which we disentangle the break dynamics of the mean and the variance parameters. In this class, the number of regimes is possibly infinite and is determined when estimating the model, thus avoiding the need to set this number by a model choice criterion. We develop a new Markov chain Monte Carlo estimation method that solves the path dependence issue due to the moving average component. Empirical results on macroeconomic series illustrate that the proposed class of models dominates the model with fixed parameters in terms of point and density forecasts.

Date: 2017
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Citations: View citations in EconPapers (15)

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Related works:
Working Paper: Autoregressive moving average infinite hidden Markov-switching models (2017)
Working Paper: Autoregressive Moving Average Infinite Hidden Markov-Switching Models (2017)
Working Paper: Autoregressive moving average infinite hidden markov-switching models (2015)
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DOI: 10.1080/07350015.2015.1123636

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