Predictive Econometrics and Big Data
Editors: Kreinovich, Vladik, Sriboonchitta, Songsak, Chakpitak, Nopasit (Eds.)
Free Preview- Presents recent research on Predictive Econometrics and Big Data
- Introduces readers to the theoretical foundations and applications
- Written by respected experts in the field
- Includes edited papers presented at the 11th International Conference of the Thailand Econometric Society (TES2018), held in Chiang Mai, Thailand, on January 10-12, 2018
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- About this book
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This book presents recent research on predictive econometrics and big data. Gathering edited papers presented at the 11th International Conference of the Thailand Econometric Society (TES2018), held in Chiang Mai, Thailand, on January 10-12, 2018, its main focus is on predictive techniques – which directly aim at predicting economic phenomena; and big data techniques – which enable us to handle the enormous amounts of data generated by modern computers in a reasonable time. The book also discusses the applications of more traditional statistical techniques to econometric problems.
Econometrics is a branch of economics that employs mathematical (especially statistical) methods to analyze economic systems, to forecast economic and financial dynamics, and to develop strategies for achieving desirable economic performance. It is therefore important to develop data processing techniques that explicitly focus on prediction. The more data we have, the better our predictions will be. As such, these techniques are essential to our ability to process huge amounts of available data.
- Table of contents (55 chapters)
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Data in the 21st Century
Pages 3-17
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Model-Assisted Survey Estimation with Imperfectly Matched Auxiliary Data
Pages 21-35
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COBra: Copula-Based Portfolio Optimization
Pages 36-77
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Multiple Testing of One-Sided Hypotheses: Combining Bonferroni and the Bootstrap
Pages 78-94
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Exploring Message Correlation in Crowd-Based Data Using Hyper Coordinates Visualization Technique
Pages 95-121
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Table of contents (55 chapters)
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Bibliographic Information
- Bibliographic Information
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- Book Title
- Predictive Econometrics and Big Data
- Editors
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- Vladik Kreinovich
- Songsak Sriboonchitta
- Nopasit Chakpitak
- Series Title
- Studies in Computational Intelligence
- Series Volume
- 753
- Copyright
- 2018
- Publisher
- Springer International Publishing
- Copyright Holder
- Springer International Publishing AG, part of Springer Nature
- eBook ISBN
- 978-3-319-70942-0
- DOI
- 10.1007/978-3-319-70942-0
- Hardcover ISBN
- 978-3-319-70941-3
- Softcover ISBN
- 978-3-319-89018-0
- Series ISSN
- 1860-949X
- Edition Number
- 1
- Number of Pages
- XII, 780
- Number of Illustrations
- 146 b/w illustrations
- Topics
