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

February 28, 2020

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Format: Chicago

Marijn A. Bolhuis, and Brett Rayner. The More the Merrier? A Machine Learning Algorithm for Optimal Pooling of Panel Data, (USA: International Monetary Fund, 2020) accessed December 22, 2024

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.

Summary

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.

Subject: Machine learning, Production, Production growth, Technology

Keywords: Algorithm, Bias-variance tradeoff, Country, Country of interest, Eastern Europe, Example country, Forecasts, GDP growth, Machine learning, Machine learning method, Macroeconomic aggregate, Panel data, Pooling, Production growth, Proximate country, WP

Publication Details

Pages:

21
Volume:

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DOI:

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Issue:

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Series:

Working Paper No. 2020/044
Stock No:

WPIEA2020044
ISBN:

9781513529974
ISSN:

1018-5941

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