Bayesian forecasting is a natural product of a Bayesian approach to inference. The Bayesian approach in general …. In Bayesian forecasting, one simply takes a subset of the unknown quantities to be future values of some variables of … interest. This chapter presents the principles of Bayesian forecasting, and describes recent advances in computational …

We provide methods for forecasting variables and predicting turning points in panel Bayesian VARs. We specify a … point and average forecasts are provided. An application to the problem of forecasting the growth rate of output and of … predicting turning points in the G-7 illustrates the approach. A comparison with alternative forecasting methods is also provided. …

Standard VAR and Bayesian VAR models are proven to be reliable tools for modeling and forecasting, yet they are still … specifications of the VAR and BVAR models for the IP and Euribor series provide with better forecasting performance. Interestingly …

We present an estimation and forecasting method, based on a differential evolution MCMC method, for inference in GARCH … nearly all series. Finally, we carry out a forecasting exercise to evaluate the usefulness of structural break models. …

Accurate and robust short-term load forecasting plays a significant role in electric power operations. This paper … load forecasting problem. The objective is to automatically generate models that could effectively and reliably predict … problem of forecasting electricity consumption. This study, besides providing an important contribution to the energy load …

This Paper provides a novel approach to forecasting time series subject to discrete structural breaks. We propose a …