We model economic policy uncertainty (EPU) in the four largest euro area countries by applying machine learning techniques to news articles. The unsupervised machine learning algorithm used makes it possible to retrieve the individual components of overall EPU endogenously for a wide range of...

We present evidence that referenda have a significant, detrimental outcome on investment. Employing an unsupervised machine learning algorithm over the period 2008-2017, we construct three important uncertainty indices underlying reports in the Scottish news media: Scottish independence...

We estimate a FAVAR with Bayesian techniques in order to investigate the impact of loan supply conditions on euro area corporate investment and its financing structure. We identify shocks to overall demand and loan supply with sign and impact restrictions. Although tightened financial conditions...

This paper investigates the link between corporate debt and investment for a group of five peripheral euro area countries. Using firm-level data from 2005-2014, we postulate a non-linear corporate leverage-investment relationship and derive thresholds beyond which leverage has a negative and...

We develop early warning models for financial crisis prediction by applying machine learning techniques to macrofinancial data for 17 countries over 1870–2016. Most nonlin-ear machine learning models outperform logistic regression in out-of-sample predictions and forecasting. We identify...

Why do long-run interest rates respond to central bank communication? Whereas existing explanations imply a common set of signals drives short and long-run yields, we show that news on economic uncertainty can have increasingly large effects along the yield curve. To evaluate this channel, we...

We develop a measure of overall financial risk in China by applying machine learning techniques to textual data. A pre-defined set of relevant newspaper articles is first selected using a specific constellation of risk-related keywords. Then, we employ topical modelling based on an unsupervised...

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...

To date, there has been little investigation of the impact of seasonal adjustment on the detection of business cycle expansion and recession regimes. We study this question both analytically and through Monte Carlo simulations. Analytically, we view the occurrence of a single business cycle...