Machine-learning techniques in economics :: new tools for predicting economic growth /: Atin Basuchoudary, James T. Bang, Tinni Sen.

Basuchoudhary, Atin,; Bang, James T.,; Sen, Tinni,

doi:10.1007/978-3-319-69014-8

Machine-learning techniques in economics : new tools for predicting economic growth / Atin Basuchoudary, James T. Bang, Tinni Sen.

Basuchoudhary, Atin, author.; Bang, James T., author.; Sen, Tinni, author.

2017

HD82 .B37 2017

Available Online

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Title

Machine-learning techniques in economics : new tools for predicting economic growth / Atin Basuchoudary, James T. Bang, Tinni Sen.

Author

Basuchoudhary, Atin, author.

ISBN

9783319690148 (electronic book)
3319690140 (electronic book)
9783319690131
3319690132

DOI

https://doi.org/10.1007/978-3-319-69014-8

Published

Cham, Switzerland : Springer, [2017]

Language

English

Description

1 online resource : illustrations.

Item Number

10.1007/978-3-319-69014-8 doi

Call Number

HD82 .B37 2017

Dewey Decimal Classification

338.9

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. .-- Provided by publisher.

Bibliography, etc. Note

Includes bibliographical references.

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Access limited to authorized users.

Digital File Characteristics

text file PDF

Source of Description

Vendor-supplied metadata.

Added Author

Bang, James T., author.
Sen, Tinni, author.

Series

SpringerBriefs in economics.