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Robustness in Econometrics

  • Book
  • © 2017

Overview

Editors:
  1. Vladik Kreinovich

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

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  2. Songsak Sriboonchitta

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

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    You can also search for this editor in PubMed   Google Scholar

  3. Van-Nam Huynh

    1. Japan Adv. Inst. of Sci. & Tech. (JAIST) , Ishikawa, Japan

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    You can also search for this editor in PubMed   Google Scholar

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

Part of the book series: Studies in Computational Intelligence (SCI, volume 692)

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Table of contents (43 chapters)

  1. Front Matter

    Pages i-x
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  2. Keynote Addresses

    1. Front Matter

      Pages 1-1
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    2. Robust Estimation of Heckman Model

      • Elvezio Ronchetti
      Pages 3-21

  3. Fundamental Theory

    1. Front Matter

      Pages 23-23
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    2. Sequential Monte Carlo Sampling for State Space Models

      • Mario V. Wüthrich
      Pages 25-50

    3. Robustness as a Criterion for Selecting a Probability Distribution Under Uncertainty

      • Songsak Sriboonchitta, Hung T. Nguyen, Vladik Kreinovich, Olga Kosheleva
      Pages 51-68

    4. Why Cannot We Have a Strongly Consistent Family of Skew Normal (and Higher Order) Distributions

      • Thongchai Dumrongpokaphan, Vladik Kreinovich
      Pages 69-77

    5. Econometric Models of Probabilistic Choice: Beyond McFadden’s Formulas

      • Olga Kosheleva, Vladik Kreinovich, Songsak Sriboonchitta
      Pages 79-87

    6. How to Explain Ubiquity of Constant Elasticity of Substitution (CES) Production and Utility Functions Without Explicitly Postulating CES

      • Olga Kosheleva, Vladik Kreinovich, Thongchai Dumrongpokaphan
      Pages 89-98

    7. How to Make Plausibility-Based Forecasting More Accurate

      • Kongliang Zhu, Nantiworn Thianpaen, Vladik Kreinovich
      Pages 99-110

    8. Structural Breaks of CAPM-type Market Model with Heteroskedasticity and Quantile Regression

      • Cathy W. S. Chen, Khemmanant Khamthong, Sangyeol Lee
      Pages 111-134

    9. Weighted Least Squares and Adaptive Least Squares: Further Empirical Evidence

      • Martin Sterchi, Michael Wolf
      Pages 135-167

    10. Prior-Free Probabilistic Inference for Econometricians

      • Ryan Martin
      Pages 169-186

    11. Robustness in Forecasting Future Liabilities in Insurance

      • W. Y. Jessica Leung, S. T. Boris Choy
      Pages 187-200

    12. On Conditioning in Multidimensional Probabilistic Models

      • Radim Jiroušek
      Pages 201-216

    13. New Estimation Method for Mixture of Normal Distributions

      • Qianfang Hu, Zheng Wei, Baokun Li, Tonghui Wang
      Pages 217-233

    14. EM Estimation for Multivariate Skew Slash Distribution

      • Weizhong Tian, Guodong Han, Tonghui Wang, Varith Pipitpojanakarn
      Pages 235-248

    15. Constructions of Multivariate Copulas

      • Xiaonan Zhu, Tonghui Wang, Varith Pipitpojanakarn
      Pages 249-265

    16. Plausibility Regions on the Skewness Parameter of Skew Normal Distributions Based on Inferential Models

      • Xiaonan Zhu, Ziwei Ma, Tonghui Wang, Teerawut Teetranont
      Pages 267-286

    17. International Yield Curve Prediction with Common Functional Principal Component Analysis

      • Jiejie Zhang, Ying Chen, Stefan Klotz, Kian Guan Lim
      Pages 287-304
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Keywords

About this book

This book presents recent research on robustness in econometrics. Robust data processing techniques – i.e., techniques that yield results minimally affected by outliers – and their applications to real-life economic and financial situations are the main focus of this book. The book also discusses applications of more traditional statistical techniques to econometric problems.


Econometrics is a branch of economics that uses mathematical (especially statistical) methods to analyze economic systems, to forecast economic and financial dynamics, and to develop strategies for achieving desirable economic performance. In day-by-day data, we often encounter outliers that do not reflect the long-term economic trends, e.g., unexpected and abrupt fluctuations. As such, it is important to develop robust data processing techniques that can accommodate these fluctuations.


Editors and Affiliations

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

    Vladik Kreinovich

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

    Songsak Sriboonchitta

  • Japan Adv. Inst. of Sci. & Tech. (JAIST) , Ishikawa, Japan

    Van-Nam Huynh

Bibliographic Information

  • Book Title: Robustness in Econometrics

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

  • Series Title: Studies in Computational Intelligence

  • DOI: https://doi.org/10.1007/978-3-319-50742-2

  • Publisher: Springer Cham

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer International Publishing AG 2017

  • Hardcover ISBN: 978-3-319-50741-5Published: 20 February 2017

  • Softcover ISBN: 978-3-319-84480-0Published: 13 July 2018

  • eBook ISBN: 978-3-319-50742-2Published: 11 February 2017

  • Series ISSN: 1860-949X

  • Series E-ISSN: 1860-9503

  • Edition Number: 1

  • Number of Pages: X, 705

  • Number of Illustrations: 9 b/w illustrations, 120 illustrations in colour

  • Topics: Computational Intelligence, Artificial Intelligence, Econometrics

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