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Optimal Forecasts from Markov Switching Models

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  • Tom Boot
  • Andreas Pick

Abstract

We derive forecasts for Markov switching models that are optimal in the mean square forecast error (MSFE) sense by means of weighting observations. We provide analytic expressions of the weights conditional on the Markov states and conditional on state probabilities. This allows us to study the effect of uncertainty around states on forecasts. It emerges that, even in large samples, forecasting performance increases substantially when the construction of optimal weights takes uncertainty around states into account. Performance of the optimal weights is shown through simulations and an application to U.S. GNP, where using optimal weights leads to significant reductions in MSFE. Supplementary materials for this article are available online.

Suggested Citation

  • Tom Boot & Andreas Pick, 2018. "Optimal Forecasts from Markov Switching Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(4), pages 628-642, October.
    • Handle: RePEc:taf:jnlbes:v:36:y:2018:i:4:p:628-642
    DOI: 10.1080/07350015.2016.1219264
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    Cited by:

    1. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2024. "Forecasting the UK top 1% income share in a shifting world," Economica, London School of Economics and Political Science, vol. 91(363), pages 1047-1074, July.
    2. Langevin, R.;, 2024. "Consistent Estimation of Finite Mixtures: An Application to Latent Group Panel Structures," Health, Econometrics and Data Group (HEDG) Working Papers 24/16, HEDG, c/o Department of Economics, University of York.

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