Causal Inference in Econometrics (Hardcover, 1st ed. 2016)

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.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Series:	Studies in Computational Intelligence, 622
Release date:	2016
First published:	2016
Editors:	Van-Nam Huynh • Vladik Kreinovich • Songsak Sriboonchitta
Dimensions:	235 x 155 x 35mm (L x W x T)
Format:	Hardcover
Pages:	638
Edition:	1st ed. 2016
ISBN-13:	978-3-319-27283-2
Categories:	Books > Business & Economics > Economics > Econometrics > General
LSN:	3-319-27283-7
Barcode:	9783319272832

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