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000827644 020__ $$a9783319690148$$q(electronic book)
000827644 020__ $$a3319690140$$q(electronic book)
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000827644 050_4 $$aHD82$$b.B37 2017
000827644 08204 $$a338.9$$223
000827644 1001_ $$aBasuchoudhary, Atin,$$eauthor.
000827644 24510 $$aMachine-learning techniques in economics :$$bnew tools for predicting economic growth /$$cAtin Basuchoudary, James T. Bang, Tinni Sen.
000827644 264_1 $$aCham, Switzerland :$$bSpringer,$$c[2017]
000827644 300__ $$a1 online resource :$$billustrations.
000827644 336__ $$atext$$btxt$$2rdacontent
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000827644 4901_ $$aSpringerBriefs in economics,$$x2191-5512
000827644 504__ $$aIncludes bibliographical references.
000827644 5050_ $$aWhy this Book? -- Data, Variables, and Their Sources -- Methodology -- Predicting Economic Growth: A First Look -- Predicting Economic Growth: Which Variables Matter? -- Predicting Recessions: What We Learn from Widening the Goalposts -- Epilogue.
000827644 506__ $$aAccess limited to authorized users.
000827644 520__ $$aThis book develops a machine-learning framework for predicting economic growth. It can also be considered as a primer for using machine learning (also known as data mining or data analytics) to answer economic questions. While machine learning itself is not a new idea, advances in computing technology combined with a dawning realization of its applicability to economic questions makes it a new tool for economists. .--$$cProvided by publisher.
000827644 588__ $$aVendor-supplied metadata.
000827644 650_0 $$aEconomic development$$xForecasting.
000827644 650_0 $$aEconomic forecasting.
000827644 650_0 $$aMachine learning.
000827644 7001_ $$aBang, James T.,$$eauthor.
000827644 7001_ $$aSen, Tinni,$$eauthor.
000827644 77608 $$iPrint version:$$aBasuchoudhary, Atin.$$tMachine-learning techniques in economics.$$dCham, Switzerland : Springer, [2017]$$z3319690132$$z9783319690131$$w(OCoLC)1004094882
000827644 830_0 $$aSpringerBriefs in economics.
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