Introduction: On the 15th of June 2020, we have 7,984,067 total COVID-19 cases, globally and 435,181 deaths. Ethiopia was ranked 2nd and 15th in the table by 176 new cases and by 3,521 total new cases from African countries. Then, this study aimed to predict COVID-19 new cases and new deaths...

We develop efficient simulation techniques for Bayesian inference on switching GARCH models. Our contribution to existing literature is manifold. First, we discuss different multi-move sampling techniques for Markov Switching (MS) state space models with particular attention to MS-GARCH models....

This paper advances the application of Bayesian graphical structural vector autoregressive (BGSVAR) models to address the problem of impulse response estimation in VAR-based systems. The BGSVAR is designed as a robust empirical framework for impulse response estimation using information from the...

We present an estimation and forecasting method, based on a differential evolution MCMC method, for inference in GARCH models subjected to an unknown number of structural breaks at unknown dates. We treat break dates as parameters and determine the number of breaks by computing the marginal...

We consider forecast combination and, indirectly, model selection for VAR models when there is uncertainty about which variables to include in the model in addition to the forecast variables. The key difference from traditional Bayesian variable selection is that we also allow for uncertainty...

We propose new forecast combination schemes for predicting turning points of business cycles. The combination schemes deal with the forecasting performance of a given set of models and possibly providing better turning point predictions. We consider turning point predictions generated by...

This paper presents the Matlab package DeCo (Density Combination) which is based on the paper by Billio et al. (2013) where a constructive Bayesian approach is presented for combining predictive densities originating from different models or other sources of information. The combination weights...

This paper designs and implements a Baynesian dynamic latent factor model for a vector of data describing the Iowa economy. Posterior distributions of parameters and the latentfactor are analyzed by Markov Chain Monte Carlo methods, and coincident and leading indicators are given by posterior...

A Bayesian semi-parametric dynamic model combination is proposed in order to deal with a large set of predictive densities. It extends the mixture of experts and the smoothly mixing regression models by allowing combination weight dependence between models as well as over time. It introduces an...