This paper discusses how the forecast accuracy of a Bayesian vector autoregression (BVAR) is affected by introducing … the zero lower bound on the federal funds rate. As a benchmark I adopt a common BVAR specification, including 18 variables …

area, the United States and Japan. In particular, incorporating survey forecast information helps to reduce the uncertainty …

entropy to tilt one-step ahead and long-horizon VAR forecasts to match the nowcast and long-horizon forecast from the Survey … of Professional Forecasters. The results indicate meaningful gains in multi-horizon forecast accuracy relative to model …, including those that are not directly tilted but are affected through spillover effects from tilted variables. The forecast …

accurate BVAR model used to forecast external demand provides an unbiased forecast and also yields a better forecast of turning … external demand forecast faster than is currently possible. The external demand forecast helps to forecast exports and, through … with the help thereof - their external demand, relying on BVAR models and using monthly time series (confidence indices …

, (ii) aggregate demand and (iii) oil-specific demand shock, by proposing the Information Criterion model averaging as a … demand shock, and more persistent following an oil specific demand shock …

We propose a novel and numerically efficient quantification approach to forecast uncertainty of the real price of oil …, estimation of time-varying forecast biases and facets of miscalibration of individual forecast densities and time-varying inter …-varying forecast uncertainty and risk for the real price of oil over the period 1974-2018. We show that the combination approach …

This paper evaluates the real-time forecast performance of alternative Bayesian Vector Autoregressive (VAR) models for … models with more flexible error covariance structures forecast GDP growth and inflation better than the standard VAR, while …