Random and Markov switching exponential smoothing models

Mike Tsionas

Technological Forecasting and Social Change, 2022, vol. 174, issue C

Abstract: 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.

Keywords: Random switching exponential smoothing; Forecasting; Bayesian analysis; Markov chain Monte Carlo (search for similar items in EconPapers)
JEL-codes: C11 C13 C22 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:174:y:2022:i:c:s0040162521007022

DOI: 10.1016/j.techfore.2021.121268

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