In many countries, a sizable share of international trade is carried out by intermediaries. While large firms tend to export to foreign markets directly, smaller firms typically export via intermediaries (indirect exporting). I document a set of facts that characterize the dynamic nature of...

We construct a new database which covers production and trade in 136 primary commodities and 24 manufacturing and service sectors for 145 countries. Using this new more granular data, we estimate spillover effects from plausible trade fragmentation scenarios in a new multi-country, multi-sector,...

Conceptual ambiguities and statistical weaknesses hamper the assessment of external competitiveness. The term competitiveness, while applied extensively, is often imprecisely defined, which can result in analytical errors and mistaken policy advice. Furthermore, aggregate statistical measures of...

We leverage insights from machine learning to optimize the trade off between bias and variance when estimating economic models using pooled datasets. Specifically, we develop a simple algorithm that estimates the similarity of economic structures across countries and selects the optimal pool of...

We propose a framework to link empirical models of systemic risk to theoretical network/general equilibrium models used to understand the channels of transmission of systemicrisk. The theoretical model allows for systemic risk due to interbank counterpartyrisk, common asset exposures/fire sales,...

Contrary to the traditional assumption of interest rates on government debt exceeding economicgrowth, negative interest-growth differentials have become prevalent since the global financialcrisis. As these differentials are a key determinant of public debt dynamics, can we sleep moresoundly,...

We analyse euro area investors' portfolio rebalancing during the ECB's Asset Purchase Programme at the security level. Our empirical analysis shows that euro area investors (in particular investment funds and households) actively rebalanced away from securities targeted under the Public Sector...

We develop a framework to nowcast (and forecast) economic variables with machine learning techniques. We explain how machine learning methods can address common shortcomings of traditional OLS-based models and use several machine learning models to predict real output growth with lower forecast...

To identify and quantify downside risks to housing markets, we apply the house price-at-risk methodology to a sample of 37 cities across the United States and Canada using quarterly data from 1983 to 2018. This paper finds that downside risks to housing markets in the United States have...