Machine-learning Techniques in Economics : New Tools for Predicting Economic Growth / by Atin Basuchoudhary, James T. Bang, Tinni Sen

Von:

Basuchoudhary, Atin [aut]

Mitwirkende(r):

Resource type: Ressourcentyp: Buch (Online)Buch (Online)Sprache: Englisch Reihen: SpringerBriefs in Economics | SpringerLink Bücher | Springer eBook Collection Economics and FinanceVerlag: Cham : Springer, 2017Beschreibung: Online-Ressource (VI, 94 p. 20 illus., 19 illus. in color, online resource)ISBN:

9783319690148

Schlagwörter:

Andere physische Formen: 9783319690131 | Erscheint auch als: Machine-learning techniques in economics. Druck-Ausgabe 1st edition 2018. Cham : Springer, 2017. vi, 91 Seiten | Printed edition: 9783319690131 DDC-Klassifikation:

338.9

LOC-Klassifikation:

HD72-88

DOI: DOI: 10.1007/978-3-319-69014-8Online-Ressourcen:

Zusammenfassung: This book develops a machine-learning framework for predicting economic growth. It can also be considered as a primer for using machine learning (also known as data mining or data analytics) to answer economic questions. While machine learning itself is not a new idea, advances in computing technology combined with a dawning realization of its applicability to economic questions makes it a new tool for economists.Zusammenfassung: Why this Book? -- Data, Variables, and Their Sources -- Methodology -- Predicting Economic Growth: A First Look -- Predicting Economic Growth: Which Variables Matter? -- Predicting Recessions: What We Learn from Widening the Goalposts -- EpiloguePPN: PPN: 165862338XPackage identifier: Produktsigel: ZDB-2-ECF

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