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Machine-learning techniques in econo...

Bang, James T.

Machine-learning techniques in economicsnew tools for predicting economic growth /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine-learning techniques in economicsby Atin Basuchoudhary, James T. Bang, Tinni Sen.
Reminder of title:	new tools for predicting economic growth /
Author:	Basuchoudhary, Atin.
other author:	Bang, James T.
Published:	Cham :Springer International Publishing :2017.
Description:	vi, 94 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Economic developmentForecasting.
Online resource:	http://dx.doi.org/10.1007/978-3-319-69014-8
ISBN:	9783319690148$q(electronic bk.)

Machine-learning techniques in economicsnew tools for predicting economic growth /
Basuchoudhary, Atin.

Machine-learning techniques in economicsnew tools for predicting economic growth /[electronic resource] :by Atin Basuchoudhary, James T. Bang, Tinni Sen. - Cham :Springer International Publishing :2017. - vi, 94 p. :ill., digital ;24 cm. - Springerbriefs in economics,2191-5504. - Springerbriefs in economics..

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.

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.

ISBN: 9783319690148$q(electronic bk.)

Standard No.: 10.1007/978-3-319-69014-8doiSubjects--Topical Terms:

462031
Economic development
--Forecasting.

LC Class. No.: HD82 / .B37 2017

Dewey Class. No.: 338.9

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