We leverage insights from machine learning to optimize the tradeoff 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 countries to maximize out-of-sample prediction accuracy of a
model. We apply the new alogrithm by nowcasting output growth with a panel of 102
countries and are able to significantly improve forecast accuracy relative to alternative pools.
The algortihm improves nowcast performance for advanced economies, as well as emerging
market and developing economies, suggesting that machine learning techniques using pooled
data could be an important macro tool for many countries.