Corruption is macro-relevant for many countries, but is often hidden, making measurement of it-and its effects-inherently difficult. Existing indicators suffer from several weaknesses, including a lack of time variation due to the sticky nature of perception-based measures, reliance on a limited...

Vessel traffic data based on the Automatic Identification System (AIS) is a big data source for nowcasting trade activity in real time. Using Malta as a benchmark, we develop indicators of trade and maritime activity based on AIS-based port calls. We test the quality of these indicators by...

The COVID-19 pandemic underscores the critical need for detailed, timely information on its evolving economic impacts, particularly for Sub-Saharan Africa (SSA) where data availability and lack of generalizable nowcasting methodologies limit efforts for coordinated policy responses. This paper...

This paper synthesizes four lessons from the experiences of six Asian e-money schemes for central banks as they consider adopting central bank digital currency (CBDC): (i) CBDC should embody four attributes: trust, convenience, efficiency, and security; (ii) CBDC service providers can facilitate...

The COVID-19 crisis has had a tremendous economic impact for all countries. Yet, assessing the full impact of the crisis has been frequently hampered by the delayed publication of official GDP statistics in several emerging market and developing economies. This paper outlines a machine-learning...

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 compared the predictive performance of a series of machine learning and traditional methods for monthly CDS spreads, using firms' accounting-based, market-based and macroeconomics variables for a time period of 2006 to 2016. We find that ensemble machine learning methods (Bagging, Gradient...

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...

Recent advances in digital technology and big data have allowed FinTech (financial technology)lending to emerge as a potentially promising solution to reduce the cost of credit and increasefinancial inclusion. However, machine learning (ML) methods that lie at the heart of FinTech credithave...