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27 December 2019 GDP Analysis and Comparison in Coastal Cities Based on Time Series Analysis
Yuxin Shuai, Zhefang Zhou
Author Affiliations +
Abstract

Shuai, Y. and Zhou, Z., 2019. GDP analysis and comparison in coastal cities based on time series analysis. In: Li, L.; Wan, X.; and Huang, X. (eds.), Recent Developments in Practices and Research on Coastal Regions: Transportation, Environment and Economy. Journal of Coastal Research, Special Issue No. 98, pp. 402–406. Coconut Creek (Florida), ISSN 0749-0208.

Gross domestic product (GDP) is an index to measure the comprehensive strength of a region. It is one of the most important economic indicators, which makes it worthy of further studies and research. As economic development areas and the only places in mainland China that have a stock exchange, the economic growth of the coastal cities Shenzhen and Shanghai is experiencing many opportunities and challenges. Because Shanghai and Shenzhen have the only two stock exchanges in mainland China, the economic behavior in those cities could somehow represent the economic behavior of China. The comparison and analysis of GDP data in Shenzhen and Shanghai is meaningful. GDP data from 1979 to 2018 in the two cities was collected from the National Bureau of Statistics of China. According to the Granger causality test, the GDP data of two cities cannot provide statistically significant information for each other. The autoregressive integrated moving average (ARIMA)(2,2,3) model was constructed for the GDP data of Shenzhen and ARIMA(1,2,3) for the GDP data of Shanghai. The logarithm of the GDP data series becomes stationary. The model fits the GDP data series well, and the predicted GDP growth of Shenzhen is slightly greater than for Shanghai.

©Coastal Education and Research Foundation, Inc. 2019
Yuxin Shuai and Zhefang Zhou "GDP Analysis and Comparison in Coastal Cities Based on Time Series Analysis," Journal of Coastal Research 98(sp1), 402-406, (27 December 2019). https://doi.org/10.2112/SI98-091.1
Received: 10 August 2019; Accepted: 28 September 2019; Published: 27 December 2019
KEYWORDS
ARIMA model
Granger causality test
time series analysis
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