Causal Inference in Econometrics / Studies in Computational Intelligence Bd.622 (PDF)
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...
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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.
- 2015, 1st ed. 2016, 638 Seiten, Englisch
- Herausgegeben: Van-Nam Huynh, Vladik Kreinovich, Songsak Sriboonchitta
- Verlag: Springer-Verlag GmbH
- ISBN-10: 3319272845
- ISBN-13: 9783319272849
- Erscheinungsdatum: 28.12.2015
Abhängig von Bildschirmgrösse und eingestellter Schriftgrösse kann die Seitenzahl auf Ihrem Lesegerät variieren.
- Dateiformat: PDF
- Grösse: 14 MB
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