12 datasets found

Using forecasts from Consensus Economics Inc., we provide evidence on the efficiency of real GDP growth forecasts by testing whether forecast revisions are uncorrelated. As the...

We estimate the EU Commission loss preferences for major economic forecasts of 12 Member States. Based on a recently proposed method by Elliott, Komunjer and Timmermann (2005)...

The aim of this paper is to assess whether modeling structural change can help improving the accuracy of macroeconomic forecasts. We conduct a simulated real-time out-of-sample...

Detection of structural change is a critical empirical activity, but continuous monitoring for changes in real time raises well-known econometric issues that have been explored...

This paper develops methods for automatic selection of variables in Bayesian vector autoregressions (VARs) using the Gibbs sampler. In particular, I provide computationally...

In this paper we discuss how the point and density forecasting performance of Bayesian vector autoregressions (BVARs) is affected by a number of specification choices. We adopt...

Several statistical issues that arise in the construction and interpretation of measures of uncertainty from forecast surveys that include probability questions are considered,...

We examine matched point and density forecasts of output growth, inflation and unemployment from the ECB Survey of Professional Forecasters. We construct measures of uncertainty...

This paper proposes a framework to implement regression-based tests of predictive ability in unstable environments, including, in particular, forecast unbiasedness and...

This paper introduces classification tree ensembles (CTEs) to the banking crisis forecasting literature. I show that CTEs substantially improve out-of-sample forecasting...

Many forecasts are conditional in nature. For example, a number of central banks routinely report forecasts conditional on particular paths of policy instruments. Even though...

Empirical work in macroeconometrics has been mostly restricted to using vector autoregressions (VARs), even though there are strong theoretical reasons to consider general...