Download This PaperThe More the Merrier? A Machine Learning Algorithm for Optimal Pooling of Panel Data
22 Pages Posted: 24 Apr 2020
Date Written: February 2020
Abstract
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 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.
Keywords: Economic models, Production growth, Developing countries, Emerging markets, Data analysis, Machine learning, GDP growth, forecasts, panel data, pooling., WP, forecast error, DGP, forecast, economic structure, output growth
JEL Classification: C53, C45, E01, C5, L31, F16, C33
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