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雷达卡
by Atin Basuchoudhary (Author), James T. Bang (Author), Tinni Sen (Author)
About this book
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.
Table of contents
- Front Matter
- Why This Book?
- Data, Variables, and Their Sources
- Methodology
- Predicting a Country’s Growth: A First Look
- Predicting Economic Growth: Which Variables Matter
- Predicting Recessions: What We Learn from Widening the Goalposts
- Back Matter
Series: SpringerBriefs in Economics
Paperback: 94 pages
Publisher: Springer; 1st ed. 2017 edition (December 28, 2017)
Language: English
About the series
SpringerBriefs present concise summaries of cutting-edge research and practical applications across a wide spectrum of fields. Featuring compact volumes of 50 to 125 pages, the series covers a range of content from professional to academic. Typical topics might include:
- A timely report of state-of-the art analytical techniques
- A bridge between new research results, as published in journal articles, and a contextual literature review
- A snapshot of a hot or emerging topic
- An in-depth case study or clinical example
- A presentation of core concepts that students must understand in order to make independent contributions
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