Prediction and Causality in Econometrics and Related Topics

1. Front Matter

   Pages i-xi

   PDF

2. Theoretical Results

   1. Front Matter

      Pages 1-1

      PDF

   2. On Random Sets for Inference in Statistics and Econometrics

      • Hung T. Nguyen

      Pages 3-21

   3. How the Proportion of People Who Agree to Perform a Task Depends on the Stimulus: A Theoretical Explanation of the Empirical Formula

      • Laxman Bokati, Vladik Kreinovich, Doan Thanh Ha

      Pages 22-27

   4. How to Explain the Anchoring Formula in Behavioral Economics

      • Laxman Bokati, Vladik Kreinovich, Chon Van Le

      Pages 28-34

   5. Models Only Say What They’re Told to Say

      • William M. Briggs

      Pages 35-42

   6. Lüders Rule and Conditional Probability for Commuting Events

      • John Harding, Hung Nguyen

      Pages 43-56

   7. On Solving Image Deblurring Problem via Nash Equilibrium

      • Wachirapong Jirakitpuwapat, Poom Kumam, Nuttapol Pakkaranang

      Pages 57-66

   8. Commonsense Explanations of Sparsity, Zipf Law, and Nash’s Bargaining Solution

      • Olga Kosheleva, Vladik Kreinovich, Kittawit Autchariyapanikul

      Pages 67-74

   9. Practical Steps to Improve Specification Testing

      • Austin Nichols, Mark E. Schaffer

      Pages 75-88

   10. Why Most Empirical Distributions Are Few-Modal

       • Julio C. Urenda, Olga Kosheleva, Vladik Kreinovich

       Pages 89-96

   11. A Different Kind of Effect Size Based on Samples from Two Populations with Delta Log-Skew-Normal Distributions

       • Ziyuan Wang, Tonghui Wang, David Trafimow, Zhaohui Xu

       Pages 97-112

   12. Efficiency Effects in a Copula Based Stochastic Frontier Model

       • Woraphon Yamaka

       Pages 113-122

   13. Why LASSO, Ridge Regression, and EN: Explanation Based on Soft Computing

       • Woraphon Yamaka, Hamza Alkhatib, Ingo Neumann, Vladik Kreinovich

       Pages 123-130

   14. Turning Points of Environmental Kuznets Curves for G20 Economies

       • Kejia Yan, Rakesh Gupta

       Pages 131-141

3. Practical Applications

   1. Front Matter

      Pages 143-143

      PDF

   2. ARIMA Model – Vietnam’s GDP Forecasting

      • Lê Thị Thúy Hằng, Nguyễn Xuân Dũng

      Pages 145-151

   3. Neural Network Models for Inflation Forecasting: A Revisit

      • Thi Thanh Xuan Pham, Tu D. Q. Le, Tien Nhat Nguyen

      Pages 152-168

   4. Impact of Financial Development on International Trade in ASEAN-6 Countries: A Bayesian Approach

      • Nguyen Ngoc Thach, Vo Thi Thuy Kieu, Duong Thi Thuy An

      Pages 169-184

   5. Determinants of Bank Liquidity: Evidence from Vietnam

      • Hai Nam Pham, Thi Hong Nhung Pham

      Pages 185-196

This book provides the ultimate goal of economic studies to predict how the economy develops—and what will happen if we implement different policies. To be able to do that, we need to have a good understanding of what causes what in economics. Prediction and causality in economics are the main topics of this book's chapters; they use both more traditional and more innovative techniques—including quantum ideas -- to make predictions about the world economy (international trade, exchange rates), about a country's economy (gross domestic product, stock index, inflation rate), and about individual enterprises, banks, and micro-finance institutions: their future performance (including the risk of bankruptcy), their stock prices, and their liquidity. Several papers study how COVID-19 has influenced the world economy.


This book helps practitioners and researchers to learn more about prediction and causality in economics -- and to further develop this important research direction.

• Computational Intelligence
• Intelligent Systems
• Econometrics
• Financial Applications
• ECONVN2021

• Banking University Ho Chi Minh City, Ho Chi Minh, Vietnam

  Nguyen Ngoc Thach

• Banking University of Ho Chi Minh City, Ho Chi Minh, Vietnam

  Doan Thanh Ha, Nguyen Duc Trung

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

  Vladik Kreinovich

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