Machine-learning Techniques in Economics

New Tools for Predicting Economic Growth
1. Auflage 2017
ISBN: 978-3-319-69013-1
Verlag: Springer-Verlag GmbH

Buch, Englisch, 94 Seiten, Book, Format (B × H): 156 mm x 236 mm, Gewicht: 179 g

Reihe: SpringerBriefs in Economics

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.

Zielgruppe

Research

Autoren/Hrsg.

Basuchoudhary, Atin

Bang, James T.

Sen, Tinni

Fachgebiete

• Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Wirtschaftswachstum
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Geistes- und Sozialwissenschaften
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
• Mathematik | Informatik Mathematik Operations Research Spieltheorie
• Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Ökonometrie

Inhaltsverzeichnis

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.- Epilogue.