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The More the Merrier? A Machine Learning Algorithm for Optimal Pooling of Panel Data
Author:
Marijn A. Bolhuis null

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Brett Rayner null

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Series:
IMF Working Papers
Volume/Issue:
Volume 2020: Issue 044
Language:
English
Publication Date:
28 Feb 2020
DOI:
https://doi.org/10.5089/9781513529974.001
ISBN:
9781513529974
ISSN:
1018-5941
Pages:
21
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
Legal-disclaimer:
IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.
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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.
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Cover IMF Working Papers
IMF Working Papers
ISSN:
1018-5941
Publisher:
International Monetary Fund