We present the results of an experiment where a random subset of the participants in the Bundesbank's household panel receive personalized response scales, centered at each participant's point forecast. Personalized response scales offer two advantages over the standard scale which is centered...

It has been recently documented that survey-based point predictions outperform even the most successful forecasting models. However, corresponding variance forecasts are frequently diagnosed as being heavily distorted. Forecasters who report inconspicuously low ex-ante variances often produce...

Using data from the European Central Bank's Survey of Professional Forecasters, we analyze the role of ex-ante conditioning variables for macroeconomic forecasts. In particular, we test to which extent the heterogeneity, updating and ex-post performance of predictions for inflation, real GDP...

We propose to treat survey-based density expectations as compositional data when testing either for heterogeneity in density forecasts across different groups of agents or for changes over time. Monte Carlo simulations show that the proposed test has more power relative to both a bootstrap...

We investigate the question of whether macroeconomic variables contain information about future stock volatility beyond that contained in past volatility. We show that forecasts of GDP growth from the Federal Reserve's Survey of Professional Forecasters predict volatility in a cross-section of...

Although survey-based point predictions have been found to outperform successful forecasting models, corresponding variance forecasts are frequently diagnosed as heavily distorted. Forecasters who report inconspicuously low ex-ante variances often produce squared forecast errors that are much...

Although survey-based point predictions have been found to outperform successful forecasting models, corresponding variance forecasts are frequently diagnosed as heavily distorted. Forecasters who report inconspicuously low ex-ante variances often produce squared forecast errors that are much...