Forecasting has always been at the forefront of decision making and planning. The
uncertainty that surrounds the future is both exciting and challenging, with individuals and …

Forecast combinations have flourished remarkably in the forecasting community and, in
recent years, have become part of mainstream forecasting research and activities …

Bayesian model averaging is flawed in the M-open setting in which the true data-generating
process is not one of the candidate models being fit. We take the idea of stacking from the …

The novel coronavirus (COVID-19) that was first reported at the end of 2019 has impacted
almost every aspect of life as we know it. This paper focuses on the incidence of the disease …

The Bayesian statistical paradigm provides a principled and coherent approach to
probabilistic forecasting. Uncertainty about all unknowns that characterize any forecasting …

This article provides guidance on how to evaluate and improve the forecasting ability of
models in the presence of instabilities, which are widespread in economic time series …

Increasingly, professional forecasters and academic researchers present model-based and
subjective or judgment-based forecasts in economics which are accompanied by some …

I discuss recent research advances in Bayesian state-space modeling of multivariate time
series. A main focus is on the “decouple/recouple” concept that enables application of state …

Decision-guided perspectives on model uncertainty expand traditional statistical thinking
about managing, comparing, and combining inferences from sets of models. Bayesian …

We propose a novel and numerically efficient quantification approach to forecast uncertainty
of the real price of oil using a combination of probabilistic individual model forecasts. Our …