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Random and Markov switching exponential smoothing models

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  • Tsionas, Mike G.

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.

Suggested Citation

  • Tsionas, Mike G., 2022. "Random and Markov switching exponential smoothing models," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    • Handle: RePEc:eee:tefoso:v:174:y:2022:i:c:s0040162521007022
    DOI: 10.1016/j.techfore.2021.121268
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    References listed on IDEAS

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    More about this item

    Keywords

    Random switching exponential smoothing; Forecasting; Bayesian analysis; Markov chain Monte Carlo;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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