標(biāo)題: Titlebook: Machine-learning Techniques in Economics; New Tools for Predic Atin Basuchoudhary,James T. Bang,Tinni Sen Book 2017 The Author(s) 2017 Mach [打印本頁] 作者: Espionage 時間: 2025-3-21 16:38
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
作者: Peak-Bone-Mass 時間: 2025-3-21 22:20 作者: conifer 時間: 2025-3-22 00:55 作者: 不愛防注射 時間: 2025-3-22 07:10 作者: 表兩個 時間: 2025-3-22 11:50 作者: 厚顏無恥 時間: 2025-3-22 14:26
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作者: Latency 時間: 2025-3-22 20:52 作者: Debark 時間: 2025-3-22 23:57 作者: BOGUS 時間: 2025-3-23 02:13
,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.作者: 羊欄 時間: 2025-3-23 07:08
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.?.作者: cardiac-arrest 時間: 2025-3-23 10:22
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作者: JAMB 時間: 2025-3-23 14:00 作者: Homocystinuria 時間: 2025-3-23 18:57
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.作者: 范圍廣 時間: 2025-3-23 23:41 作者: 嘴唇可修剪 時間: 2025-3-24 03:00
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 作者: 半身雕像 時間: 2025-3-24 09:26
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作者: 上流社會 時間: 2025-3-24 13:47
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.?.作者: crucial 時間: 2025-3-24 18:04 作者: 埋葬 時間: 2025-3-24 21:35
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.作者: Grandstand 時間: 2025-3-25 00:00 作者: Demulcent 時間: 2025-3-25 05:55
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