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Causal Inference in Econometrics

  • Book
  • © 2016

Overview

Editors:
  1. Van-Nam Huynh

    1. School of Knowledge Science, Japan Advanced Ins. of Sci. & Tech, Ishikawa, Japan

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    You can also search for this editor in PubMed   Google Scholar

  2. Vladik Kreinovich

    1. Department of Computer Science, University of Texas at El Paso, El Paso, USA

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    You can also search for this editor in PubMed   Google Scholar

  3. Songsak Sriboonchitta

    1. Faculty of Economics, Chiang Mai University, Chiangmai, Thailand

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    You can also search for this editor in PubMed   Google Scholar

  • theoretical foundations and applications
  • Written by experts in the field
  • Presents recent research
  • Includes supplementary material: sn.pub/extras

Part of the book series: Studies in Computational Intelligence (SCI, volume 622)

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Table of contents (40 chapters)

  1. Front Matter

    Pages i-xi
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  2. Fundamental Theory

    1. Front Matter

      Pages 1-1
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    2. Validating Markov Switching VAR Through Spectral Representations

      • Monica Billio, Maddalena Cavicchioli
      Pages 3-15

    3. Rapid Optimal Lag Order Detection and Parameter Estimation of Standard Long Memory Time Series

      • G. S. Dissanayake
      Pages 17-28

    4. Spatial Econometric Analysis: Potential Contribution to the Economic Analysis of Smallholder Development

      • Renato Villano, Euan Fleming, Jonathan Moss
      Pages 29-55

    5. Consistent Re-Calibration in Yield Curve Modeling: An Example

      • Mario V. Wüthrich
      Pages 57-82

    6. Autoregressive Conditional Duration Model with an Extended Weibull Error Distribution

      • Rasika P. Yatigammana, S. T. Boris Choy, Jennifer S. K. Chan
      Pages 83-107

    7. Across-the-Board Spending Cuts Are Very Inefficient: A Proof

      • Vladik Kreinovich, Olga Kosheleva, Hung T. Nguyen, Songsak Sriboonchitta
      Pages 109-118

    8. Invariance Explains Multiplicative and Exponential Skedactic Functions

      • Vladik Kreinovich, Olga Kosheleva, Hung T. Nguyen, Songsak Sriboonchitta
      Pages 119-131

    9. Why Some Families of Probability Distributions Are Practically Efficient: A Symmetry-Based Explanation

      • Vladik Kreinovich, Olga Kosheleva, Hung T. Nguyen, Songsak Sriboonchitta
      Pages 133-152

    10. The Multivariate Extended Skew Normal Distribution and Its Quadratic Forms

      • Weizhong Tian, Cong Wang, Mixia Wu, Tonghui Wang
      Pages 153-169

    11. Multiple Copula Regression Function and Directional Dependence Under Multivariate Non-exchangeable Copulas

      • Zheng Wei, Tonghui Wang, Daeyoung Kim
      Pages 171-184

    12. On Consistency of Estimators Based on Random Set Vector Observations

      • Zheng Wei, Tonghui Wang, Baokun Li
      Pages 185-198

    13. Brief Introduction to Causal Compositional Models

      • Radim JirouÅ¡ek
      Pages 199-211

    14. A New Proposal to Predict Corporate Bankruptcy in Italy During the 2008 Economic Crisis

      • Francesca di Donato, Luciano Nieddu
      Pages 213-223

  3. Applications

    1. Front Matter

      Pages 225-225
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    2. The Inflation Hedging Ability of Domestic Gold in Malaysia

      • Hooi Hooi Lean, Geok Peng Yeap
      Pages 227-241

    3. To Determine the Key Factors for Citizen in Selecting a Clinic/Division in Thailand

      • Lee Tzong-Ru (Jiun-Shen), Kanchana Chokethaworn, Huang Man-Yu
      Pages 243-254

    4. ARIMA Versus Artificial Neural Network for Thailand’s Cassava Starch Export Forecasting

      • Warut Pannakkong, Van-Nam Huynh, Songsak Sriboonchitta
      Pages 255-277

    5. Copula Based Volatility Models and Extreme Value Theory for Portfolio Simulation with an Application to Asian Stock Markets

      • Apiwat Ayusuk, Songsak Sriboonchitta
      Pages 279-293
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Keywords

About this book

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.

Editors and Affiliations

  • School of Knowledge Science, Japan Advanced Ins. of Sci. & Tech, Ishikawa, Japan

    Van-Nam Huynh

  • Department of Computer Science, University of Texas at El Paso, El Paso, USA

    Vladik Kreinovich

  • Faculty of Economics, Chiang Mai University, Chiangmai, Thailand

    Songsak Sriboonchitta

Bibliographic Information

  • Book Title: Causal Inference in Econometrics

  • Editors: Van-Nam Huynh, Vladik Kreinovich, Songsak Sriboonchitta

  • Series Title: Studies in Computational Intelligence

  • DOI: https://doi.org/10.1007/978-3-319-27284-9

  • Publisher: Springer Cham

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer International Publishing Switzerland 2016

  • Hardcover ISBN: 978-3-319-27283-2Published: 06 January 2016

  • Softcover ISBN: 978-3-319-80108-7Published: 30 March 2018

  • eBook ISBN: 978-3-319-27284-9Published: 28 December 2015

  • Series ISSN: 1860-949X

  • Series E-ISSN: 1860-9503

  • Edition Number: 1

  • Number of Pages: XI, 638

  • Number of Illustrations: 91 b/w illustrations, 15 illustrations in colour

  • Topics: Computational Intelligence, Quantitative Finance, Quality Control, Reliability, Safety and Risk

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