The More the Merrier? A Machine Learning Algorithm for Optimal Pooling of Panel Data

Marijn Bolhuis and Brett Rayner

No 2020/044, IMF Working Papers from International Monetary Fund

Abstract: 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.

Keywords: WP; country; algorithm; Machine learning; GDP growth; forecasts; panel data; pooling; proximate country; machine learning method; example country; macroeconomic aggregate; bias-variance tradeoff; country of interest; Production growth; Eastern Europe (search for similar items in EconPapers)
Pages: 21
Date: 2020-02-28
New Economics Papers: this item is included in nep-big, nep-cmp and nep-for
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