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Machine Learning for Econometrics and Related Topics

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  • © 2024

Overview

Editors:
  1. Vladik Kreinovich

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

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    You can also search for this editor in PubMed   Google Scholar

  2. 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

  3. Woraphon Yamaka

    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

  • Describes the use of more traditional econometric techniques
  • Focuses on the use of machine learning in economics
  • Includes applications of economics in agriculture, health, manufacturing, trade, and transportation

Part of the book series: Studies in Systems, Decision and Control (SSDC, volume 508)

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Table of contents (32 chapters)

  1. Front Matter

    Pages i-ix
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  2. An Invitation to Wasserstein Distance in Financial Risk Measures

    • Hung T. Nguyen
    Pages 1-26

  3. What Makes a Good Model?

    • William M. Briggs
    Pages 27-35

  4. Quantifying Fairness and Discrimination in Predictive Models

    • Arthur Charpentier
    Pages 37-77

  5. Slow-Growing Trees

    • Philippe Goulet Coulombe
    Pages 79-98

  6. Metrics on Probability Distributions Through Optimal Commuting Maps

    • Brendan Pass
    Pages 99-107

  7. Using Machine Learning Methods to Estimate the Gender Wage Gap

    • Rachel Forshaw, Vsevolod Iakovlev, Mark E. Schaffer, Cristina Tealdi
    Pages 109-129

  8. Forecasting Market Index of Stock Exchange of Thailand, Malaysia, and Singapore with the Gaussian Process Regression Model

    • Wilawan Srichaikul, Somsak Chanaim
    Pages 131-142

  9. The a Priori Procedure (APP) for Estimating Regression Coefficients in Multiple Linear Model with Skew Normal Errors

    • Tingting Tong, Xiangfei Chen, Tonghui Wang, David Trafimow, S. T. Boris Choy, Cong Wang
    Pages 143-159

  10. Why Rectified Linear Unit Is Efficient in Machine Learning: One More Explanation

    • Barnabas Bede, Vladik Kreinovich, Uyen Pham
    Pages 161-167

  11. Why Shapley Value and Its Variants Are Useful in Machine Learning (and in Other Applications)

    • Laxman Bokati, Olga Kosheleva, Vladik Kreinovich, Nguyen Ngoc Thach
    Pages 169-174

  12. A Possible Common Mechanism Behind Skew Normal Distributions in Economics and Hydraulic Fracturing-Induced Seismicity

    • Laxman Bokati, Aaron Velasco, Vladik Kreinovich, Kittawit Autchariyapanitkul
    Pages 175-179

  13. Why Quantile Regression Works Well in Economics: A Partial Explanation

    • Olga Kosheleva, Vassilis G. Kaburlasos, Vladik Kreinovich, Roengchai Tansuchat
    Pages 181-186

  14. Trade with Heterogeneous Firms, Distance, and Time: An Analysis of Latin American and the Caribbean (LAC) Manufacturing Firms

    • Sunil Dash
    Pages 187-218

  15. Analysis and Prediction of Suitable Model for Coconut Production Estimates in South Indian States

    • S. P. Gayathri, Shine Raju Kappil, S. Vijayalakshmi, Ajeet Kumar Sahoo
    Pages 219-230

  16. Portfolio Management of SET50 Stocks Using Deep Reinforcement Learning Methods

    • Nachattapong Kaewsompong, Worrawat Saijai, Sukrit Thongkairat
    Pages 231-242

  17. Household Characteristics and the Pattern of Gambling, Alcohol and Tobacco Expenditures

    • Supanika Leurcharusmee, Anaspree Chaiwan
    Pages 243-257

  18. Effects of Household Income and Parental Absence on Investment in Child Education in Thailand: Evidence from Quantile-on-Quantile Approach

    • Supanika Leurcharusmee, Jirakom Sirisrisakulchai
    Pages 259-275

  19. The Spatial Spillover Effects of Transportation Infrastructure on Economic Development in China

    • Paravee Maneejuk, Ran Jiao
    Pages 277-287

  20. Analyzing the Survival Probability of Community Enterprise Under the Situation of the COVID-19 Pandemic in Northern Thailand

    • Parevee Maneejuk, Pichayakone Rakpho
    Pages 289-302
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Keywords

About this book

In the last decades, machine learning techniques – especially techniques of deep learning – led to numerous successes in many application areas, including economics. The use of machine learning in economics is the main focus of this book; however, the book also describes the use of more traditional econometric techniques. Applications include practically all major sectors of economics: agriculture, health (including the impact of Covid-19), manufacturing, trade, transportation, etc. Several papers analyze the effect of age, education, and gender on economy – and, more generally, issues of fairness and discrimination.

We hope that this volume will:

help practitioners to become better knowledgeable of the state-of-the-art econometric techniques, especially techniques of machine learning,

and help researchers to further develop these important research directions. We want to thank all the authors for their contributions and all anonymous referees for their thorough analysis and helpful comments.

Editors and Affiliations

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

    Vladik Kreinovich

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

    Songsak Sriboonchitta, Woraphon Yamaka

Bibliographic Information

  • Book Title: Machine Learning for Econometrics and Related Topics

  • Editors: Vladik Kreinovich, Songsak Sriboonchitta, Woraphon Yamaka

  • Series Title: Studies in Systems, Decision and Control

  • DOI: https://doi.org/10.1007/978-3-031-43601-7

  • Publisher: Springer Cham

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024

  • Hardcover ISBN: 978-3-031-43600-0Published: 02 June 2024

  • Softcover ISBN: 978-3-031-43603-1Due: 03 July 2024

  • eBook ISBN: 978-3-031-43601-7Published: 01 June 2024

  • Series ISSN: 2198-4182

  • Series E-ISSN: 2198-4190

  • Edition Number: 1

  • Number of Pages: IX, 499

  • Number of Illustrations: 13 b/w illustrations, 87 illustrations in colour

  • Topics: Mechanical Engineering, Economics, general, Mathematical and Computational Engineering

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