Titlebook: Machine-learning Techniques in Economics; New Tools for Predic Atin Basuchoudhary,James T. Bang,Tinni Sen Book 2017 The Author(s) 2017 Mach

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Atin Basuchoudhary,James T. Bang,Tinni SenOffers 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 usi

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Why This Book?,. However, most of all, these algorithms can eliminate variables that are unlikely to be causal. Policy makers can therefore get a sense of the most important policy levers to change the path of growth.

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2191-5504 ing 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.?.978-3-319-69013-1978-3-319-69014-8Series ISSN 2191-5504 Series E-ISSN 2191-5512

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2191-5504 e the researcher in applying machine learning techniques usiThis 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 learn

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Book 2017 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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Methodology,scribe the process through which individual variables can be rank ordered according to their predictive power. This is followed by a description of how the reader might discern the effect of any one of these variables on the target variable, i.e. growth and recessions.

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