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 (作者)

Guangdong University of Education

Xuefen Zhong (作者)

Guangdong University of Education

Siqi Zhou (作者)

Guangdong University of Education

DOI：https://doi.org/10.63944/xqd51042

关键词：

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

已出版

2025-12-31

摘要: 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.

参考文献

[1] Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: Forecasting and control (5th ed.). Wiley.
[2] Chatterjee, S., & Hadi, A. S. (2012). Regression analysis by example (5th ed.). John Wiley &
Sons.
[3] Fujita, M., & Hu, D. (2001). Regional disparity in China 1985–1994: The effects of globalization and economic liberalization. The Annals of Regional Science, 35(1), 3–37. https://doi.org/10.1007/s001680000020
[4] Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning (2nd ed.). Springer.https://web.stanford.edu/~hastie/ElemStatLearn/
[5] Hoerl, A. E., & Kennard, R. W. (1970). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12(1), 55–67.https://doi.org/10.1080/00401706.1970.10488634
[6] Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and practice (2nd ed.). OTexts. https://otexts.com/fpp2/
[7] Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018). Statistical and machine learning forecasting methods: Concerns and ways forward. PLOS ONE, 13(3), e0194889. https://doi.org/10.1371/journal.pone.0194889
[8] Wang, S., & Zhao, D. (2020). Detecting outliers in economic time series using visual and statistical methods. Open Journal of Statistics, 10(5), 433–449. https://doi.org/10.4236/ojs.2020.105029
[9] Yuan, F., & Li, Q. (2022). Data imputation in economic modeling: An application of linear interpolation and PCA. Econometrics, 10(1), 10. https://doi.org/10.3390/econometrics10010010
[10] Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2), 301–320. https://doi.org/10.1111/j.1467-9868.2005.00503.x
[11] Zhang, G., Patuwo, B. E., & Hu, M. Y. (1998). Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting, 14(1), 35–62. https://doi.org/10.1016/S0169-2070(97)00044-7
[12] Timmermann, A. (2006). Forecast combinations. In G. Elliott, C. Granger, & A. Timmermann (Eds.), Handbook of Economic Forecasting (Vol. 1, pp. 135–196). Elsevier. https://doi.org/10.1016/S1574-0706(05)01004-9
[13] Perone, G. (2021). Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast COVID-19 hospitalizations in Italy. Health Economics Review, 11(1), 8. https://doi.org/10.1007/s10198-021-01347-4
[14] Ahmadianfar, I., et al. (2025). A hybrid framework: SVD + kernel ridge regression for river water level forecasting. Scientific Reports, 15(1), 1654. https://www.nature.com/articles/s41598-025-90628-6
[15] Zhang, T., & Wang, Y. (2023). Mixed ARIMA-LSTM model for energy consumption forecasting in China. Energies, 16(4), 1987. https://doi.org/10.3390/en16041987
[16] Taleb, N. N. (2007). The black swan: The impact of the highly improbable. Random House.
[17] Chien, T., & Zhang, L. (2021). Spatiotemporal prediction of regional economic growth using machine learning. Sustainability, 13(19), 10987. https://doi.org/10.3390/su131910987
[18] Liu, Y., & Yu, H. (2022). Comparative study of ARIMA and Prophet for GDP forecasting in emerging markets. Forecasting, 4(2), 300–315. https://doi.org/10.3390/forecast4020017
[19] Chen, S., & Shi, Y. (2020). Forecasting urban economic growth using hybrid models: A comparative study. Applied Economics Letters, 27(20), 1697–1701. https://doi.org/10.1080/13504851.2019.1707772
[20] Han, X., & Wang, M. (2023). Ridge regression for regional GDP modeling under multicollinearity. Econometrics, 11(1), 6. https://doi.org/10.3390/econometrics11010006
[21] Jolliffe, I. T., & Cadima, J. (2016). Principal component analysis: A review and recent developments. Philosophical Transactions of the Royal Society A, 374(2065), 20150202. https://doi.org/10.1098/rsta.2015.0202
[22] OECD. (2020). International migration outlook 2020. OECD Publishing. https://www.oecdilibrary.org/social-issues-migration-health/international-migration-outlook-2020_ec98f531-en
[23] Xu, X., & Zhang, M. (2020). Comparative development of global Bay Areas: The case of Tokyo and GBA. Sustainability, 12(12), 4991. https://doi.org/10.3390/su12124991
[24] Chen, Y., & Lu, M. (2021). China’s regional economic transformation and the role of Bay Area economies. Sustainability, 13(6), 3451. https://doi.org/10.3390/su13063451
[25] Rennings, K. (2000). Redefining innovation — eco-innovation research and the contribution from ecological economics. Ecological Economics, 32(2), 319–332. https://doi.org/10.1016/S0921-8009(99)00112-3

Economic Forecasting for Greater Bay Area: Ridge Regression & ARIMA

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