Abstract
This paper determined the optimal order of FGM (1, 1) model through particle swarm optimization algorithm and combined with the World Bank business environment data to predict and analyze the business environment of economies along the Belt and Road. The empirical results show that the FGM (1, 1) model has a good predicting effect on the business environment. In terms of prediction accuracy, the FGM (1, 1) model based on particle swarm optimization algorithm to determine the optimal order is significantly better than the traditional GM (1, 1) model. The predict results show that the business environment level of economies along the Belt and Road will increase year by year from 2021 to 2022, but the overall level is still relatively low. The main innovation of this paper lies in the introduction of the fractional-order grey model into the predictive analysis of the business environment, which is of great significance to the extension and application of fractional-order models in management and economic systems.
Highlights
In 2013, the Chinese government put forward the Belt and Road initiative, aiming to promote the reasonable flow of factors and effective allocation of resources in the economies along the Belt and Road, improve the level of regional trade and investment facilitation, and explore a new model of international and regional economic cooperation [1]. e Belt and Road initiative has effectively promoted the investment and cooperation between China and countries along the Belt and Road [2]
Compared with the traditional GM (1, 1) model, the FGM (1, 1) model has better prediction ability in terms of business environment. erefore, this paper chooses to predict and analyze the business environment of 52 economies along the Belt and Road based on the FGM (1, 1) model
The average Mean Percentage Error (MAPE) values of the FGM (1, 1) model is lower than that of the GM (1, 1) model, indicating that the FGM (1, 1) model has better predictive ability in terms of business environment compared with the traditional GM (1, 1) model
Summary
In 2013, the Chinese government put forward the Belt and Road initiative, aiming to promote the reasonable flow of factors and effective allocation of resources in the economies along the Belt and Road, improve the level of regional trade and investment facilitation, and explore a new model of international and regional economic cooperation [1]. e Belt and Road initiative has effectively promoted the investment and cooperation between China and countries along the Belt and Road [2]. Due to the significant differences in the development stages, cultural backgrounds, historical traditions, and institutional conditions of the economies along the Belt and Road, when enterprises conduct transnational investment activities in economies along the Belt and Road, they face the challenges of political, economic, and other macrolevel risks and face the test of the host country’s business environment. Many scholars used the FGM (1, 1) model and the extended model to predict the time series data in different fields, and the error could be effectively reduced by selecting the appropriate fractional order. This paper makes use of the transnational panel data of the World Bank on the business environment from 2014 to 2020 and uses the FGM (1, 1) model to predict and analyze the business environment of 52 economies along the Belt and Road. E rest of this paper is structured as follows: e second part introduces the method of the fractional-order grey model. e third part is the empirical analysis and the discussion of the results, including the determination of the optimal order of the FGM (1, 1) model, the comparison of the prediction accuracy between the GM (1, 1) model and FGM (1, 1) model, and the prediction results of the FGM (1, 1) model. e fourth part is the conclusion
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