The More the Merrier? : A Machine Learning Algorithm for Optimal Pooling of Panel Data /
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 count...
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| Format: | Journal |
| Language: | English |
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Washington, D.C. :
International Monetary Fund,
2020.
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| Series: | IMF Working Papers; Working Paper ;
No. 2020/044 |
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| Online Access: | Full text available on IMF |
| 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. |
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| Item Description: | <strong>Off-Campus Access:</strong> No User ID or Password Required <strong>On-Campus Access:</strong> No User ID or Password Required |
| Physical Description: | 1 online resource (21 pages) |
| Format: | Mode of access: Internet |
| ISSN: | 1018-5941 |
| Access: | Electronic access restricted to authorized BRAC University faculty, staff and students |