We present improved out-of-sample predictability of future fund flows using state-of-the-art machine learning methods. Nonlinear machine learning models significantly outperform linear models in terms of out-of-sample R-squared. Using interpretable ML methods, we identify past flows and the...

We investigate whether satellite observations of nitrogen dioxide (NO₂) - a short-lived pollutant primarily emitted by fossil fuel combustion - can improve the forecasting of oil demand. After retrieving, cleaning, and aggregating daily satellite data, we integrate NO₂ into a range of...

We present the Chicago Fed Labor Market Indicators (LMI): a twice-monthly release that includes the job-finding r ate, the j ob-separation r ate, and a f orecast f or the U.S. Bureau of Labor Statistics (BLS) unemployment rate. To overcome limitations in data availability, the LMI uses partial...