We nowcast world trade using machine learning, distinguishing between tree-based methods (random forest, gradient boosting) and their regression-based counterparts (macroeconomic random forest, linear gradient boosting). While much less used in the literature, the latter are found to outperform...

This paper exploits daily infrared images taken from satellites to track economic activity in advanced and emerging countries. We first develop a framework to read, clean, and exploit satellite images. Our algorithm uses the laws of physics (Planck's law) and machine learning to detect the heat...

We propose a Hawkish-Dovish (HD) indicator that measures the degree of 'hawkishness' or 'dovishness' of the media's perception of the ECB's tone at each press conference. We compare two methods to calculate the indicator: semantic orientation and Support Vector Machines text classification. We...

Card issuers have mainly relied on rewards programs as their main strategic driver to increase electronic payments. However, there is scarce evidence on the effectiveness of rewards programs. This paper offers novel evidence on two key issues: i) it measures the impact of rewards programs on the...

This paper analyzes the importance of retail consumers' banking relationships for loan defaults using a unique, comprehensive dataset of over one million loans by savings banks in Germany. We find that loans of retail customers, who have a relationship with their savings bank prior to applying...

Euro area GDP and components are nowcast and forecast one quarter ahead. Based on a dataset of 163 series comprising the relevant monthly indicators, simple bridge equations with one explanatory variable are estimated for each. The individual forecasts generated by each equation are then pooled,...

Several factor-based models are estimated to investigate the role of country-specific trade and survey data in forecasting euro areamanufacturing production. Following Boivin and Ng (2006), the emphasis is put on the role of dataset selection on the empirical performance of factor models. First,...

We study 30 vintages of FRB/US, the principal macro model used by the Federal Reserve Board staff for forecasting and policy analysis. To do this, we exploit archives of the model code, coefficients, baseline databases and stochastic shock sets stored after each FOMC meeting from the model's...

We suggest an alternative use of disaggregate information to forecast the aggregate variable of interest, that is to include disaggregate information or disaggregate variables in the aggregate model as opposed to first forecasting the disaggregate variables separately and then aggregating those...

This paper documents a new stylized fact of the greater macroeconomic stability of the U.S. economy over the last two decades. Using 131 monthly time series, three popular statistical methods and the forecasts of the Federal Reserve's Greenbook and the Survey of Professional Forecasters, we show...