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 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...

Forecasting macroeconomic variables is key to developing a view on a country's economic outlook.Most traditional forecasting models rely on fitting data to a pre-specified relationship between inputand output variables, thereby assuming a specific functional and stochastic process underlying...

This paper computes data-driven correlation networks based on the stock returns of international banks and conducts a comprehensive analysis of their topological properties. We first apply spatial-dependence methods to filter the effects of strong common factors and a thresholding procedure to...

This paper proposes a network model of multilaterally equilibrium exchange rates. The model introduces a topological component into the exchange rate analysis, consistently taking into account simultaneous higher-order interactions among all currencies. The paper defines the currency demand...

This paper proposes a method for assessing international spillovers from nominal demand shocks. It quantifies the impact of a shock in one country on all other countries. The paper concludes that the network effects in shock spillovers can be substantial, comparable, and often exceed the initial...

We introduce unFEAR, Unsupervised Feature Extraction Clustering, to identify economic crisis regimes. Given labeled crisis and non-crisis episodes and the corresponding features values, unFEAR uses unsupervised representation learning and a novel mode contrastive autoencoder to group episodes...

This paper examines the role of structural factors - governance and rule of law, corporate sector governance (creditor rights and shareholder rights), corporate financing structure - as well as macroeconomic variables in currency crises. Using a technique known as a binary recursive tree allows...

The paper models international spillovers from a hypothetical drop of China's imports as a result of China's rebalancing of its growth model. A network-based model used in the paper allows capturing higher round network effects of the shock, which are largely unaccounted for in the existing...

This paper studies the interconnectedness of the global financial system and its susceptibilityto shocks. A novel multilayer network framework is applied to link debt and equityexposures across countries. Use of this approach-that examines simultaneously multiplechannels of transmission and...