@book{
 title = {Machine-learning techniques in economics : New Tools for Predicting Economic Growth},
 type = {book},
 year = {2017},
 identifiers = {[object Object]},
 keywords = {Data mining,Econometrics,Economic growth,Forecasting,Machine learning,Prediction,Ranking predictive variables},
 pages = {97},
 publisher = {Springer, Cham},
 city = {Cham, Switzerland},
 id = {8d620585-b4c3-3412-88a8-4af1a8a1b383},
 created = {2018-06-29T13:50:11.363Z},
 accessed = {2018-06-29},
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 profile_id = {35f49df4-e9cf-391e-a76a-8e9e316c42aa},
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 last_modified = {2018-06-30T13:47:41.955Z},
 read = {false},
 starred = {true},
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 folder_uuids = {9f870d1a-3967-406b-94fb-b8524c625bf1},
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 abstract = {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. .-- 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.},
 bibtype = {book},
 author = {Basuchoudhary, Atin and Bang, James T. and Sen, Tinni}
}

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