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Machine-learning techniques in economics : new tools for predicting economic growth

Author: Atin Basuchoudhary; James T Bang; Tinni Sen
Publisher: Cham, Switzerland : Springer, [2017]
Series: SpringerBriefs in economics.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
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.  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Basuchoudhary, Atin.
Machine-learning techniques in economics.
Cham, Switzerland : Springer, [2017]
(OCoLC)1004094882
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Atin Basuchoudhary; James T Bang; Tinni Sen
ISBN: 9783319690148 3319690140
OCLC Number: 1017738279
Description: 1 online resource : illustrations.
Contents: 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.
Series Title: SpringerBriefs in economics.
Responsibility: Atin Basuchoudary, James T. Bang, Tinni Sen.

Abstract:

This book develops a machine-learning framework for predicting economic growth. While machine learning itself is not a new idea, advances in computing technology combined with a dawning realization  Read more...

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