Economic Forecasting for Greater Bay Area: Ridge Regression & ARIMA

Jingying Guo; Xuefen Zhong; Siqi Zhou

doi:10.63944/xqd51042

Economic Forecasting for Greater Bay Area: Ridge Regression & ARIMA

Jingying Guo (Author)

Guangdong University of Education

Xuefen Zhong (Author)

Guangdong University of Education

Siqi Zhou (Author)

Guangdong University of Education

DOI:https://doi.org/10.63944/xqd51042

Keywords:

ARIMA time series, Kmeans and Bartlett test, principal component analysis, Ridge regression model, Vif inspection

Published

2025-12-31

Abstract: This study explores the future economic trajectory of the Guangdong-Hong Kong-Macao Greater Bay Area by applying ridge regression and time series modeling techniques. To improve the reliability of the models and minimize multicollinearity issues, principal component analysis is employed to identify the most influential variables. The results suggest a stable and sustained economic growth trend for the region. A comparative analysis with Tokyo Bay reveals that while Tokyo demonstrates strengths in trade and the service sector, the Greater Bay Area shows greater potential in infrastructure expansion and technological advancement. Based on these findings, the study proposes forward-looking policy initiatives such as the development of a “digital twin” economic framework, a “talent free trade zone,” dedicated “green innovation zones,” and a “health economy corridor.” These recommendations aim to enhance the region’s economic resilience and global competitiveness. The research contributes valuable insights to support long-term planning and sustainable development in the Greater Bay Area.

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Economic Forecasting for Greater Bay Area: Ridge Regression & ARIMA

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