We develop the first agent-based model (ABM) that can compete with and in the long run significantly outperform benchmark VAR and DSGE models in out-of-sample forecasting of macro variables. Our ABM for a small open economy uses micro and macro data from national sector accounts, input-output...

I propose a novel approach to uncover business cycle reports' priorities and relate them to economic fluctuations. To this end, I leverage quantitative business-cycle forecasts published by leading German economic research institutes since 1970 to estimate the proportions of latent topics in...

This paper highlights the role of macroeconomic and financial uncertainty in predicting US recessions. In-sample forecasts using probit models indicate that these two variables are the best predictors of recessions at short horizons. Macroeconomic uncertainty has the highest predictive power up...

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...

This paper examines the extent to which errors in economic forecasts are driven by initial assumptions that prove to be incorrect ex post. Therefore, we construct a new data set comprising an unbalanced panel of annual forecasts from different institutions forecasting German GDP and the...

Using a novel data set that combines firms' qualitative survey-based sales forecasts with their quantitative balance-sheet data on realized sales, we document that only major forecast errors (those in the two tails of the distribution) are predictable and display auto-correlation. This result is...

Using a novel dataset that contains qualitative firm survey data on sales forecasts as well as balance-sheet data on realized sales, we document that only major forecast errors are predictable and display autocorrelation. This result is a particular violation of the Full Information Rational...

Using a novel dataset that contains qualitative firm survey data on sales forecasts as well as balance-sheet data on realized sales, we document that only major forecast errors are predictable and display autocorrelation. This result is a particular violation of the Full Information Rational...

We examine whether professional forecasters incorporate high-frequency information about credit conditions when revising their economic forecasts. Using Mixed Data Sampling regression approach, we find that daily credit spreads have significant predictive ability for monthly forecast revisions...

This study aims to construct a new monthly leading indicator for Tunisian economic activity and to forecast Tunisian quarterly real GDP (RGDP) using several mixed-frequency models. These include a mixed dynamic factor model, unrestricted mixed-data sampling (UMIDAS), and a threepass regression...