This paper shows entropic tilting to be a flexible and powerful tool for combining mediumterm forecasts from BVARs with short-term forecasts from other sources (nowcasts from either surveys or other models). Tilting systematically improves the accuracy of both point and density forecasts, and tilting the BVAR forecasts based on nowcast means and variances yields slightly greater gains in densit...

there are different ideas and opinions about the effects of macroeconomic variables on real and nominal variables. to answer the question of whether changes in macroeconomic variables as a political tool is useful over a business cycle, understanding the effect of macroeconomic variables on economic growth is important. in the present study, the bayesian vector autoregresive model and seasonali...

Working papers of the Federal Reserve Bank of Cleveland are preliminary materials circulated to stimulate discussion and critical comment on research in progress. They may not have been subject to the formal editorial review accorded offi cial Federal Reserve Bank of Cleveland publications. The views stated herein are those of the authors and are not necessarily those of the Federal Reserve Ban...

We incorporate external information extracted from the European Central Bank’s Survey of Professional Forecasters into predictions a Bayesian VAR using entropic tilting and soft conditioning. The resulting conditional forecasts significantly improve plain BVAR point density forecasts. Importantly, we do not restrict at specific quarterly horizon but their possible paths over several horizons jo...

The purpose of this article is to describe how inflation analysis and forecasting has been carried out in the Bank, with particular emphasis on recent research and the new challenges facing the Bank following the launch of the euro on 1 January 1999. Broadly speaking the approach adopted by the Bank over a number of years has been an eclectic one which combines judgement and a range of formal a...

This paper studies how to combine real-time forecasts from a broad range of Bayesian vector autoregression (BVAR) specifications and survey by optimally exploiting their properties. To do that, it compares the forecasting performance optimal pooling tilting techniques, including for predicting euro area inflation GDP growth at medium-term forecast horizons using both univariate multivariate met...

In this paper we review the methodology of forecasting with log-linearised DSGE models using Bayesian methods. We focus on the estimation of their predictive distributions, with special attention being paid to the mean and the covariance matrix of h-steps ahead forecasts. In the empirical analysis, we examine the forecasting performance of the New Area-Wide Model (NAWM) that has been designed f...