, thus, making them appropriate for models of large dimensions. A comprehensive forecasting exercise involving TVP-VARs of …

can lead to different conclusions on the forecasting performance of the MS-GJR model …

innovations have been well investigated. In contrast, the forecasting implications of specifying UC models with different state … studies adopting UC models for forecasting purposes. Four correlation structures for errors are entertained: orthogonal … and their connection with forecasting are discussed within a Bayesian framework. As perfectly correlated innovations …

We provide a formulation of stochastic volatility (SV) based on Gaussian process regression (GPR). Forecasting … reduces the error rate on one-year out-of-sample forecasting during the 2007-09 recession by 26% relative to a benchmark range …

tuned via an adaptive MCMC algorithm. Simulations show that the proposed models have good selection and forecasting …

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 …

, from January 1, 2010 to January 3, 2020, is the forecasting issue we explore. Design/methodology/approach - Using Bayesian … optimisations and cross-validation, we study Gaussian process (GP) regressions for our forecasting needs. Findings - The produced …

This paper is concerned with problem of variable selection and forecasting in the presence of parameter instability …. There are a number of approaches proposed for forecasting in the presence of breaks, including the use of rolling windows or … variable selection and forecasting stages. In this study, we investigate whether or not we should use weighted observations at …