Tracking online user behavior is essential for targeted advertising and is at the heart of the business model of major online platforms. We analyze tracker-specific web browsing data to show how the prediction quality of consumer profiles varies with data size and scope. We find decreasing...

I contribute to previous research on the efficient integration of forecasters' narratives into business cycle forecasts. Using a Bidirectional Encoder Representations from Transformers (BERT) model, I quantify 19,300 paragraphs from German business cycle reports (1998-2021) and classify the...

This study employs six Machine Learning methods - Logit, Lasso-Logit, Ridge-Logit, Random Forest, Extreme Gradient Boosting, and an Ensemble - alongside registry data on abortions in Spain from 2011-2019 to predict multiple abortions and assess monetary savings through targeted interventions. We...

This study employs six Machine Learning methods - Logit, Lasso-Logit, Ridge-Logit, Random Forest, Extreme Gradient Boosting, and an Ensemble - alongside registry data on abortions in Spain from 2011-2019 to predict multiple abortions and assess monetary savings through targeted interventions. We...

In this paper, I investigate how inflation signals from different types of newspapers influence household inflation expectations in Germany. Using text data and the large language model GPT-3.5-turbo-1106, I construct newspaper-specific indicators and find significant heterogeneity in their...

This paper investigates nowcasting Growth-at-Risk (GaR) using consensus forecasts from the Survey of Professional Forecasters (SPF) in the US. Incorporating SPF consensus forecasts into the conditional mean of an AR-GARCH type model significantly enhances nowcasting accuracy for GaR and the...

In this paper, I investigate how inflation signals from different types of newspapers influence household inflation expectations in Germany. Using text data and the large language model GPT-3.5-turbo-1106, I construct newspaper-specific indicators and find significant heterogeneity in their...

This paper investigates nowcasting Growth-at-Risk (GaR) using consensus forecasts from the Survey of Professional Forecasters (SPF) in the US. Incorporating SPF consensus forecasts into the conditional mean of an AR-GARCH type model significantly enhances nowcasting accuracy for GaR and the...

In this paper, we revisit the issue of excess sensitivity of consumption to income and address the weak instrument problem that is well documented in this literature. Using quarterly data for the U.S. economy, we first highlight the weak instrument problem by showing that the use of conventional...

We derive new tests for proper calibration of multivariate density forecasts based on Rosenblatt probability integral transforms. These tests have the advantage that they i) do not depend on the ordering of variables in the forecasting model, ii) are applicable to densities of arbitrary...