Estimating United States-Asia Clothing Trade: Multiple Regression vs. Artificial Neural Networks

• 발행기관: 한국유통과학회
• 저자명: Eve M. H. CHAN Danny C. K. HO C.-W. TSANG
• 간행물 정보: 『The Journal of Asian Finance, Economics and Business(JAFEB)』Vol. 8 No.7, 403~411쪽, 전체 9쪽
• 주제분류: 경제경영 > 경제학
• 파일형태: PDF
• 발행일자: 2021.07.30

영문 초록

This study discusses the influence of economic factors on the clothing exports from China and 15 South and Southeast Asian countries to the United States. A basic gravity trade model with three predictors, including the GDP value produced by exporting and importing countries and their geographical distance was established to explain the bilateral trade patterns. The conventional approach of multiple regression and the novel approach of Artificial Neural Networks (ANNs) were developed based on the value of clothing exports from 2012 to 2018 and applied to the trade pattern prediction of 2019. The results showed that ANNs can achieve a more accurate prediction in bilateral trade patterns than the commonly-used econometric analysis of the basic gravity trade model. Future studies can examine the predictive power of ANNs on an extended gravity model of trade that includes explanatory variables in social and environmental areas, such as policy, initiative, agreement, and infrastructure for trade facilitation, which are crucial for policymaking and managerial consideration. More research should be conducted for the examination of the balance between developing countries’ economic growth and their social and environmental sustainability and for the application of more advanced machine-learning algorithms of global trade flow examination.

목차

1. Introduction

2. Literature Review

3. Methodology

4. Results

5. Discussion

6. Conclusion

키워드

Artificial Neural Networks China Clothing Trade Economic Gravity Trade Model