This paper compares the behavior of subject' uncertainty in different monetary policy environments when forecasting inflation in the laboratory. We find that inflation targeting produces lower uncertainty and higher accuracy of interval forecasts than inflation forecast targeting. We also...

We construct an index for measuring negative economic sentiment in Finland by using news titles collected from the Finnish broadcasting company Yle's archive. Our approach uses supervised machine learning text classification for detecting news titles featuring negative economic sentiment, and...

We propose an imperfect information model for the expectations of macroeconomic forecasters that explains differences in average disagreement levels across forecasters by means of cross sectional heterogeneity in the variance of private noise signals. We show that the forecaster-specific...

We construct sentiment indices for 20 countries from 1980 to 2019. Relying on computational text analysis, we capture specific language like 'fear', 'risk', 'hedging', 'opinion', and, 'crisis', as well as 'positive' and 'negative' sentiments, in news articles from the Financial Times. We assess...

We describe the evolution of forecasts in the run-up to recessions. The GDP forecasts cover 63 countries for the years 1992 to 2014. The main finding is that, while forecasters are generally aware that recession years will be different from other years, they miss the magnitude of the recession...

Most macroeconomic data is continuously revised as additional information becomes available. We suggest that revisions of data is an important source of uncertainty about the state of the economy. This paper evaluates the quality of major real macroeconomic Euro area variables, published by...

Most macroeconomic data is continuously revised as additional information becomes available. We suggest that revisions of data is an increasingly important source of uncertainty about the state of the economy and offer an alternative channel of uncertainty - data uncertainty. This paper adds on...

We investigate the pricing of temperature derivatives under weather forecasts modeled by enlarged filtrations. We also treat option pricing and optimal portfolio selection in temperature markets with future information. We finally prove an anticipative sufficient stochastic minimum principle and...

We use machine learning to predict stock returns at forward horizons from 1 month ahead to 120 months ahead. Stock return predictability declines with the forecast horizon; it follows an asymptotic exponential decay process consisting of a permanent component (c. 20 bp/month) and a transient...