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

Espionage

書目名稱Machine-learning Techniques in Economics影響因子(影響力)




書目名稱Machine-learning Techniques in Economics影響因子(影響力)學(xué)科排名




書目名稱Machine-learning Techniques in Economics網(wǎng)絡(luò)公開度




書目名稱Machine-learning Techniques in Economics網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Machine-learning Techniques in Economics被引頻次




書目名稱Machine-learning Techniques in Economics被引頻次學(xué)科排名




書目名稱Machine-learning Techniques in Economics年度引用




書目名稱Machine-learning Techniques in Economics年度引用學(xué)科排名




書目名稱Machine-learning Techniques in Economics讀者反饋




書目名稱Machine-learning Techniques in Economics讀者反饋學(xué)科排名

Peak-Bone-Mass

conifer

不愛防注射

表兩個(gè)

厚顏無(wú)恥

Predicting Recessions: What We Learn from Widening the Goalposts,ct” growth variables to check whether these variables are better at predicting recessions. We show how prediction performance of algorithms differs widely depending on the type of prediction criteria. We can, however, identify some of the most salient predictors of recessions. These suggest that fis

Latency

Debark

BOGUS

,Predicting a Country’s Growth: A First Look,o validate different growth models. We suggest that validated algorithms enhance the confidence academics should place on any given theoretical growth model. We then show how machine learning can help researchers understand what kinds of concepts may make theoretical growth models more complete.

羊欄

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.?.