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Machine-learning Techniques in Economics: New Tools for Predicting Economic Growth: SpringerBriefs in Economics

Autor Atin Basuchoudhary, James T. Bang, Tinni Sen
en Limba Engleză Paperback – 8 ian 2018
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. 
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Specificații

ISBN-13: 9783319690131
ISBN-10: 3319690132
Pagini: 87
Ilustrații: VI, 94 p. 20 illus., 19 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.15 kg
Ediția:1st ed. 2017
Editura: Springer International Publishing
Colecția Springer
Seria SpringerBriefs in Economics

Locul publicării:Cham, Switzerland

Cuprins

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.

Caracteristici

Offers a guide to how machine learning techniques can improve predictive power in answering economic questions Provides R codes to help guide the researcher in applying machine learning techniques using the R package Uses partial dependence plots to tease out non-linear effects of explanatory variables on the dependent variables