TY - BOOK ID - 134099879 TI - The More the Merrier? A Machine Learning Algorithm for Optimal Pooling of Panel Data AU - Bolhuis, Marijn. AU - Rayner, Brett. PY - 2020 PB - Washington, D.C. : International Monetary Fund, DB - UniCat KW - Macroeconomics KW - Intelligence (AI) & Semantics KW - Forecasting and Other Model Applications KW - Neural Networks and Related Topics KW - Technological Change: Choices and Consequences KW - Diffusion Processes KW - Macroeconomics: Production KW - Machine learning KW - Production growth KW - Technology KW - Production KW - Economic theory KW - Costa Rica UR - https://www.unicat.be/uniCat?func=search&query=sysid:134099879 AB - 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. ER -