Causal Inference in Econometrics
Causal Inference in Econometrics
Springer | Studies in Computational Intelligence | January 29, 2016 | ISBN-10: 3319272837 | 638 pages | pdf | 14.4 mb
Springer | Studies in Computational Intelligence | January 29, 2016 | ISBN-10: 3319272837 | 638 pages | pdf | 14.4 mb
Editors: Huynh, Van-Nam, Kreinovich, Vladik, Sriboonchitta, Songsak (Eds.)
Includes theoretical foundations and applications
Written by experts in the field
Presents recent research
This book is devoted to the analysis of causal inference which is one of the most difficult tasks in data analysis: when two phenomena are observed to be related, it is often difficult to decide whether one of them causally influences the other one, or whether these two phenomena have a common cause. This analysis is the main focus of this volume.
To get a good understanding of the causal inference, it is important to have models of economic phenomena which are as accurate as possible. Because of this need, this volume also contains papers that use non-traditional economic models, such as fuzzy models and models obtained by using neural networks and data mining techniques. It also contains papers that apply different econometric models to analyze real-life economic dependencies.
Number of Illustrations and Tables
91 illus., 15 in colour
Topics
Computational Intelligence
Quantitative Finance
Quality Control, Reliability, Safety and Risk
More info and Hardcover at Springer
Purchase a Premium account to Donate & Support :)
More Science Books …Visit my Blog :)