Random and Markov switching exponential smoothing models

Abstract

• We report results from Bayesian analysis of random switching exponential smoothing models. • The new methods are robust and easy to implement. • In a Monte Carlo setting it is shown that the results are particularly encouraging and the methods perform well with real data sets. • We extend the basic model under a Markov chain assumption on the slope of the stochastic trend. • We provide tools for model comparison and model selection in terms of out-of-sample behavior. In this paper we report results from Bayesian analysis of random switching exponential smoothing models. The new methods are robust and easy to implement. In a Monte Carlo setting it is shown that the results are particularly encouraging and the methods perform well with real data sets. Moreover, we extend the basic model under a Markov chain assumption on the slope of the stochastic trend, and we provide tools for model comparison and model selection in terms of out-of-sample behavior. The models are applied to a number of U.S. time series.