Machine-learning Techniques in Economics

New Tools for Predicting Economic Growth

Atin Basuchoudhary, James T. Bang, Tinni Sen (Autoren)

Buch | Softcover

VI, 94 Seiten

2018 | 1st ed. 2017
Springer International Publishing (Verlag)
978-3-319-69013-1 (ISBN)

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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.

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.

Erscheinungsdatum	19.01.2018
Reihe/Serie	SpringerBriefs in Economics
Zusatzinfo	VI, 94 p. 20 illus., 19 illus. in color.
Verlagsort	Cham
Sprache	englisch
Maße	155 x 235 mm
Gewicht	165 g
Themenwelt	Wirtschaft ► Volkswirtschaftslehre ► Makroökonomie
Schlagworte	Computer applications in the social & behavioural • Computer applications in the social & behavioural • Computer Appl. in Social and Behavioral Sciences • Data Mining • data mining and knowledge discovery • Econometrics • econometrics & economic statistics • Econometrics & economic statistics • Economic Growth • Economics • Economics and finance • Expert systems / knowledge-based systems • Forecasting • Game Theory • machine learning • Prediction • Ranking predictive variables
ISBN-10	3-319-69013-2 / 3319690132
ISBN-13	978-3-319-69013-1 / 9783319690131
Zustand	Neuware