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...

This paper analyzes the effects of bank mergers on bank-firm relationships. Using matched bank-firm level data, I find that mergers disrupt lending relationships, specially to small borrowers of target banks. However, I find significant positive effects of mergers for borrowers that continue the...

Applying the identification strategy employed by Driscoll (2004) for the United States, this paper provides empirical evidence for the existence of a bank lending channel of monetary policy transmission in the euro area. In addition, and in contrast to recent findings for the US, we find that in...

This paper proposes a tractable way to incorporate lending standards ("credit qualification thresholds") into macro models of financial frictions. Banks can reject borrowers whose risk is above an endogenous threshold at which no lending rate sufficiently compensates banks for the borrowers'...

Do female directors on banks' boards influence lending decisions toward less polluting firms? By using granular credit register data matched with information on firm-level greenhouse gas (GHG) emission intensities, we isolate credit supply shifts and find that banks with more gender-diverse...