Optimal forecasts from Markov switching models
We derive forecasts for Markov switching models that are optimal in the 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 US GNP, where using optimal weights leads to significant reductions in MSFE.
|Keywords||Markov switching models, forecasting, optimal weights, GNP forecasting|
|JEL||Discrete Regression and Qualitative Choice Models; Discrete Regressors (jel C25), Forecasting and Other Model Applications (jel C53), Forecasting and Simulation (jel E37)|
|Persistent URL||dx.doi.org/10.1080/07350015.2016.1219264, hdl.handle.net/1765/95499|
|Journal||Journal of Business and Economic Statistics|
Boot, T, & Pick, A. (2017). Optimal forecasts from Markov switching models. Journal of Business and Economic Statistics. doi:10.1080/07350015.2016.1219264