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Machine Learning for Econometrics and Related Topics.pdf
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Among the main objectives of econometrics are to predict the future economic situation—and to provide recommendations on how to make the future economic situation as good as possible. To make a prediction and to provide appropriate recommendations, it is important to understand the economy’s dynamics. In the past, this was mostly achieved by developing special econometric models. Once a model is selected, in each situation, we can find the values of the model’s parameters that provide the best fit for the data, and then use the model with these parameters to predict the future state of the economy. This traditional approach utilizes available economic data. However, similar prediction problems occur in many application areas. It is therefore reasonable, when predicting economic dynamics, to use not only economic data, but also data from other application areas. The corresponding techniques—that try to make predictions in general situations, without using models specific for each application area—are known as techniques of machine learning. 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 the 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.
An Invitation to Wasserstein Distance in Financial Risk Measures ...... 1
Hung T. Nguyen
What Makes a Good Model? ....................................... 27
William M. Briggs
Quantifying Fairness and Discrimination in Predictive Models ......... 37
Arthur Charpentier
Slow-Growing Trees ............................................... 79
Philippe Goulet Coulombe
Metrics on Probability Distributions Through Optimal Commuting
Maps ............................................................. 99
Brendan Pass
Using Machine Learning Methods to Estimate the Gender Wage
Gap .............................................................. 109
Rachel Forshaw, Vsevolod Iakovlev, Mark E. Schaffer,
and Cristina Tealdi
Forecasting Market Index of Stock Exchange of Thailand,
Malaysia, and Singapore with the Gaussian Process Regression
Model ............................................................ 131
Wilawan Srichaikul and Somsak Chanaim
The a Priori Procedure (APP) for Estimating Regression
Coefficients in Multiple Linear Model with Skew Normal Errors ....... 143
Tingting Tong, Xiangfei Chen, Tonghui Wang, David Trafimow,
S. T. Boris Choy, and Cong Wang
Why Rectified Linear Unit Is Efficient in Machine Learning: One
More Explanation ................................................. 161
Barnabas Bede, Vladik Kreinovich, and Uyen Pham
Why Shapley Value and Its Variants Are Useful in Machine
Learning (and in Other Applications) ............................... 169
Laxman Bokati, Olga Kosheleva, Vladik Kreinovich,
and Nguyen Ngoc Thach
A Possible Common Mechanism Behind Skew Normal
Distributions in Economics and Hydraulic Fracturing-Induced
Seismicity ......................................................... 175
Laxman Bokati, Aaron Velasco, Vladik Kreinovich,
and Kittawit Autchariyapanitkul
Why Quantile Regression Works Well in Economics: A Partial
Explanation ....................................................... 181
Olga Kosheleva, Vassilis G. Kaburlasos, Vladik Kreinovich,
and Roengchai Tansuchat
Trade with Heterogeneous Firms, Distance, and Time: An Analysis
of Latin American and the Caribbean (LAC) Manufacturing Firms .... 187
Sunil Dash
Analysis and Prediction of Suitable Model for Coconut Production
Estimates in South Indian States .................................... 219
S. P. Gayathri, Shine Raju Kappil, S. Vijayalakshmi,
and Ajeet Kumar Sahoo
Portfolio Management of SET50 Stocks Using Deep Reinforcement
Learning Methods ................................................. 231
Nachattapong Kaewsompong, Worrawat Saijai, and Sukrit Thongkairat
Household Characteristics and the Pattern of Gambling, Alcohol
and Tobacco Expenditures ......................................... 243
Supanika Leurcharusmee and Anaspree Chaiwan
Effects of Household Income and Parental Absence
on Investment in Child Education in Thailand: Evidence
from Quantile-on-Quantile Approach ............................... 259
Supanika Leurcharusmee and Jirakom Sirisrisakulchai
The Spatial Spillover Effects of Transportation Infrastructure
on Economic Development in China ................................. 277
Paravee Maneejuk and Ran Jiao
Analyzing the Survival Probability of Community Enterprise
Under the Situation of the COVID-19 Pandemic in Northern
Thailand .......................................................... 289
Parevee Maneejuk and Pichayakone Rakpho
Bayesian Consideration for Trust in Ewom: Evidence from Vietnam ... 303
An Application of Explainable Artificial Intelligence in Credit
Scoring ........................................................... 317
Son Phuc Nguyen and Nhat Quang Truong
Assessing Strategies for Adaption to Survive the COVID-19
Situation of OTOP Operators in Thailand ........................... 335
Piangtawan Polard, Supareuk Tarapituxwong, and Wilawan Srichaikul
The Effects of Oil Shocks on Inflation in Leading Crude Oil
Importing Countries: Non-linear Autoregressive Distributed Lag ...... 349
Wiranya Puntoon, Payap Tarkhamtham, and Woraphon Yamaka
Contagion Effects Among Selected Asian Stock Markets During
the COVID-19 Pandemic: A Dynamic Conditional Correlation
Approach ......................................................... 361
Worrawat Saijai, Todsapn Panya, and Paravee Maneejuk
The Nexus of the Nikkei 225, Gold, and Crude Oil. Do They Have
a Co-movement in the Long Run? New Evidence for Cointegration
from the Autoregressive Distributed Lag Bounds Test ................. 375
Suppaleuk Sarpphaitoon
Does Contract Farming Improve Farmers’ Income? The Case
of Pineapple Farmers in Nong Khai and Loei, Thailand ............... 399
Teerawut Teetranont and Payap Tarkhamtham
Implications of Aging Population and Health Spending for Thai
Economic Growth ................................................. 409
Phachongchit Tibprasorn and Rungrapee Phadkantha
Portfolio Management Decision Support System Using
Cryptocurrencies and Traditional Assets in Indian Context ............ 419
S. Vijayalakshmi, Manavi Sharma, Elian Jose, and Shine Raju Kappil
Environmental, Social, and Governance (ESG) Ratings and Stock
Mispricing: A Moderated Mediation Model .......................... 435
Guochao Wan, Ahmad Yahya Dawod, Somsak Chanaim, and Chao Li
The Role of Socio-Demographics and Aging Society on the Change
of Agricultural Labor in ASEAN .................................... 455
Woraphon Yamaka and Chaiwat Klinlampu
Prediction of Financial Contagion and Spillover Effects of the US
Financial Crisis Using Google Index ................................. 467
Woraphon Yamaka and Natthanon Panyawai
The Impact of China-United States Trade War on the Relationship
Among the Exchange Rates: The Case of China, Korea, and Japan ..... 489
Liying Zhao, Dan Yao, Bing Yang, and Kongliang Zhu
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