Construction of Economic Dynamic Forecast Model for Guangdong-Hong Kong-Macao Greater Bay Area
Year: 2025
Type: conference-paper
Abstract: This study takes the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) as the research object and systematically analyzes the economic growth dynamics and evolution path of the region. This is achieved through the integration of time series modeling and multi-scenario simulation methods. The non-stationarity of the data is confirmed using the Augmented Dickey-Fuller (ADF) test. Subsequently, a vector autoregression (VAR (5)) model is developed using full point estimation (FPE), Akaike information criterion (AIC), etc. The model reveals that infrastructure investment and the tertiary industry have a short-term driving effect on gross domestic product (GDP), while R&D funding and population growth play a dominant role in the long term. It is worth noting that the impact of infrastructure investment shows a trend of diminishing marginal effects. Combined with the prediction of the second-order difference stationary autoregressive integrated moving average (ARIMA (1,2,2)) model, the elasticity coefficient of the tertiary industry will significantly affect the average annual GDP growth rate in the next 10 years. This study provides a quantitative decision-making basis for improving the economic resilience and sustainable development of the Greater Bay Area within the framework of the dual circulation strategy.
Authors: Ziyan Liu, Yantao He, Geyi Lin, Rongtao Lin
Institution: Guangdong University of Science and Technology
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4412789938