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16 February 2023 Research on port throughput based on VAR model
Yuanyuan Shi, Mingchen Gu, Jian Feng, Changjian Liu
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Proceedings Volume 12591, Sixth International Conference on Traffic Engineering and Transportation System (ICTETS 2022); 1259110 (2023) https://doi.org/10.1117/12.2668737
Event: 6th International Conference on Traffic Engineering and Transportation System (ICTETS 2022), 2022, Guangzhou, China
Abstract
The port throughput is a barometer of the world economic development and the basic premise for the competent government departments to formulate macroeconomic and industrial development policies. In order to screen out the factors that affect the port throughput more accurately and clarify the action time of each factor, this study determines the VAR model, which is famous for its medium and long-term prediction accuracy, as the main application model for analysis, and expounds the relevant theory of the model. Then, it introduces the steps of establishing the model and the evaluation indicators of the model, and then demonstrates the method in combination with the coal throughput of Chinese ports. This study selects the annual data of the port coal throughput, the added value of the third industry, the total import and export investment and other indicators from 1980 to 2020, and finally concludes that the port coal throughput of China has a strong correlation with the total import and export investment and the number of people, and the port coal throughput has a positive impact on itself and reaches the maximum in the second phase, The impact of total import and export investment on Port coal throughput is negative after reaching the maximum in phase II, and the impact of population on Port coal throughput is negative after reaching the maximum in phase III.
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Yuanyuan Shi, Mingchen Gu, Jian Feng, and Changjian Liu "Research on port throughput based on VAR model", Proc. SPIE 12591, Sixth International Conference on Traffic Engineering and Transportation System (ICTETS 2022), 1259110 (16 February 2023); https://doi.org/10.1117/12.2668737
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KEYWORDS
Autoregressive models
Data modeling
Industry
Analytical research
Machine learning
Statistical analysis
Statistical modeling
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