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  1. The more the merrier?
    a machine learning algorithm for optimal pooling of panel data
    Author: Bolhuis, Marijn A.; Rayner, Brett
    Published: 2020
    Publisher:  International Monetary Fund, [Washington, DC]

    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... more

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    Staatsbibliothek zu Berlin - Preußischer Kulturbesitz, Haus Unter den Linden
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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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    Source: Staatsbibliothek zu Berlin
    Language: English
    Media type: Ebook
    Format: Online
    ISBN: 9781513529974
    Other identifier:
    Series: IMF working paper ; WP/20, 44
    Subjects: Algorithm; Country; GDP Growth; Machine Learning; WP
    Scope: 1 Online-Ressource (circa 22 Seiten), Illustrationen
  2. The more the merrier?
    a machine learning algorithm for optimal pooling of panel data
    Author: Bolhuis, Marijn A.; Rayner, Brett
    Published: 2020
    Publisher:  International Monetary Fund, [Washington, DC]

    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... more

    Access:
    Verlag (kostenfrei)
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    Staatsbibliothek zu Berlin - Preußischer Kulturbesitz, Haus Potsdamer Straße
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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

     
    Export to reference management software   RIS file
      BibTeX file
    Source: Staatsbibliothek zu Berlin
    Language: English
    Media type: Ebook
    Format: Online
    ISBN: 9781513529974
    Other identifier:
    Series: IMF working paper ; WP/20, 44
    Subjects: Algorithm; Country; GDP Growth; Machine Learning; WP
    Scope: 1 Online-Ressource (circa 22 Seiten), Illustrationen