We produce a social unrest risk index for 125 countries covering a period of 1996 to 2020. The risk of social unrest is based on the probability of unrest in the following year derived from a machine learning model drawing on over 340 indicators covering a wide range of macro-financial,...

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...

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...

Financial crises pose unique challenges for forecast accuracy. Using the IMF's Monitoring of Fund Arrangement (MONA) database, we conduct the most comprehensive evaluation of IMF forecasts to date for countries in times of crises. We examine 29 macroeconomic variables in terms of bias,...

Against the backdrop of an ongoing review of the inflation-targeting framework, this paper examines the real-time inflation forecasts of the Bank of Canada with the aim of identifying potential areas for improvement. Not surprisingly, the results show that errors in forecasting non-core...

External headwinds, together with domestic vulnerabilities, have loomed over the prospects of emerging markets in recent years. We propose an empirical toolbox to quantify the impact of external macro-financial shocks on domestic economies in parsimonious way. Our model is a Bayesian VAR...

This paper improves short-term forecasting models of monthly tourism arrivals by estimating and evaluating a time-series model with exogenous regressors (ARIMA-X) using a case of Aruba, a small open tourism-dependent economy. Given importance of the US market for Aruba, it investigates...

This paper analyzes the inflation forecast errors over the period 2021Q1-2022Q3 using forecasts of core and headline inflation from the International Monetary Fund World Economic Outlook for a large group of advanced and emerging market economies. The findings reveal evidence of forecast bias...