02067cas a2200325 a 4500 AALejournalIMF016237 230101c9999 xx r poo 0 0eng d 5.00 USD 9781513529974 1018-5941 BD-DhAAL BD-DhAAL Bolhuis, Marijn. The More the Merrier? : A Machine Learning Algorithm for Optimal Pooling of Panel Data / Marijn Bolhuis, Brett Rayner. Washington, D.C. : International Monetary Fund, 2020. 1 online resource (21 pages) IMF Working Papers <strong>Off-Campus Access:</strong> No User ID or Password Required <strong>On-Campus Access:</strong> No User ID or Password Required Electronic access restricted to authorized BRAC University faculty, staff and students 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. Mode of access: Internet Algorithm imf Country imf GDP Growth imf Machine Learning imf WP imf Costa Rica imf Rayner, Brett. IMF Working Papers; Working Paper ; No. 2020/044 Full text available on IMF http://elibrary.imf.org/view/journals/001/2020/044/001.2020.issue-044-en.xml IMF e-Library