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  • Search: subject:"Regression Trees and Forests"
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Year of publication
Subject
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Business Cycles 2 DynamicFactor Model 2 Forecasting 2 GDP Growth 2 Machine Learning 2 Regression Trees and Forests 2 United States 2 Artificial intelligence 1 Bruttoinlandsprodukt 1 Business cycle 1 Economic growth 1 Estimation 1 Forecasting model 1 Forestry 1 Forstwirtschaft 1 Gross domestic product 1 Konjunktur 1 Künstliche Intelligenz 1 National income 1 Nationaleinkommen 1 Prognoseverfahren 1 Schätzung 1 USA 1 Wirtschaftswachstum 1
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Online availability
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Free 2
Type of publication
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Book / Working Paper 2
Type of publication (narrower categories)
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Working Paper 2 Arbeitspapier 1 Graue Literatur 1 Non-commercial literature 1
Language
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English 2
Author
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Wochner, Daniel 2
Published in...
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KOF Working Papers 1 KOF working papers 1
Source
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ECONIS (ZBW) 1 EconStor 1
Showing 1 - 2 of 2
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Dynamic Factor Trees and Forests: A theory-led machine learning framework for non-linear and state-dependent short-term U.S. GDP growth predictions
Wochner, Daniel - 2020
Machine Learning models are often considered to be "black boxes" that provide only little room for the incorporation of theory (cf. e.g. Mukherjee, 2017; Veltri, 2017). This article proposes so-called Dynamic Factor Trees (DFT) and Dynamic Factor Forests (DFF) for macroeconomic forecasting, which...
Persistent link: https://www.econbiz.de/10012546027
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Cover Image
Dynamic Factor Trees and Forests : a theory-led machine learning framework for non-linear and state-dependent short-term U.S. GDP growth predictions
Wochner, Daniel - 2020 - This version: January 31, 2020
Machine Learning models are often considered to be "black boxes" that provide only little room for the incorporation of theory (cf. e.g. Mukherjee, 2017; Veltri, 2017). This article proposes so-called Dynamic Factor Trees (DFT) and Dynamic Factor Forests (DFF) for macroeconomic forecasting, which...
Persistent link: https://www.econbiz.de/10012172506
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