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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