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Behavioral Predictive Modeling in Economics

  • Book
  • © 2021

Overview

Editors:
  1. Songsak Sriboonchitta

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

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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. Woraphon Yamaka

    1. Faculty of Economics, Center of Excellence in Econometrics, Chiang Mai University, Chiang Mai, Thailand

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  • Presents a state-of-the-art overview of what can be done and what should be done to incorporate behavioral predictive models into econometrics
  • Showcases recently developed behavioral models
  • Written by respected experts in the field

Part of the book series: Studies in Computational Intelligence (SCI, volume 897)

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Table of contents (29 chapters)

  1. Front Matter

    Pages i-ix
    Download chapter PDF

  2. Theoretical Results

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. On Modeling of Uncertainty in Behavioral Economics

      • Hung T. Nguyen
      Pages 3-21

    3. Using Machine Learning Methods to Support Causal Inference in Econometrics

      • Achim Ahrens, Christopher Aitken, Mark E. Schaffer
      Pages 23-52

    4. Don’t Test, Decide

      • William M. Briggs
      Pages 53-63

    5. Weight Determination Model for Social Networks in a Trust-Enhanced Recommender System

      • Mei Cai, Yiming Wang, Zaiwu Gong, Guo Wei
      Pages 65-85

    6. Survey-Based Forecasting: To Average or Not to Average

      • Kayan Cheng, Naijing Huang, Zhentao Shi
      Pages 87-104

    7. PD-Implied Ratings via Referencing a Credit Rating/Scoring Pool’s Default Experience

      • Jin-Chuan Duan, Shuping Li
      Pages 105-115

    8. Tail Risk Measures and Portfolio Selection

      • Young C. Joo, Sung Y. Park
      Pages 117-139

    9. Why Beta Priors: Invariance-Based Explanation

      • Olga Kosheleva, Vladik Kreinovich, Kittawit Autchariyapanitkul
      Pages 141-144

    10. Ranking-Based Voting Revisited: Maximum Entropy Approach Leads to Borda Count (and Its Versions)

      • Olga Kosheleva, Vladik Kreinovich, Guo Wei
      Pages 145-152

    11. Monitoring Change Points by a Sign Change Method

      • Bo-Ru Lai, Hsiuying Wang
      Pages 153-170

    12. Find Trade Patterns in China’s Stock Markets Using Data Mining Agents

      • Baokun Li, Ziwei Ma, Tonghui Wang
      Pages 171-179

    13. The Decomposition of Quadratic Forms Under Matrix Variate Skew-Normal Distribution

      • Ziwei Ma, Tonghui Wang, Baokun Li, Xiaonan Zhu, Yuede Ma
      Pages 181-194

    14. How to Gauge a Combination of Uncertainties of Different Type: General Foundations

      • Ingo Neumann, Vladik Kreinovich, Thach Ngoc Nguyen
      Pages 195-201

    15. Conditional Dependence Among Oil, Gold and U.S. Dollar Exchange Rates: A Copula-GARCH Approach

      • Zheng Wei, Zijing Zhang, HongKun Zhang, Tonghui Wang
      Pages 203-218

    16. Extremal Properties and Tail Asymptotic of Alpha-Skew-Normal Distribution

      • Weizhong Tian, Huihui Li, Rui Huang
      Pages 219-233
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Keywords

About this book

This book presents both methodological papers on and examples of applying behavioral predictive models to specific economic problems, with a focus on how to take into account people's behavior when making economic predictions. This is an important issue, since traditional economic models assumed that people make wise economic decisions based on a detailed rational analysis of all the relevant aspects. However, in reality – as Nobel Prize-winning research has shown – people have a limited ability to process information and, as a result, their decisions are not always optimal. Discussing the need for prediction-oriented statistical techniques, since many statistical methods currently used in economics focus more on model fitting and do not always lead to good predictions, the book is a valuable resource for researchers and students interested in the latest results and challenges and for practitioners wanting to learn how to use state-of-the-art techniques. 

Editors and Affiliations

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

    Songsak Sriboonchitta

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

    Vladik Kreinovich

  • Faculty of Economics, Center of Excellence in Econometrics, Chiang Mai University, Chiang Mai, Thailand

    Woraphon Yamaka

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