Abstract
Business survey, which starts from the microeconomic level, is a widely used short-term forecasting tool in practice. In this study, the authors examine whether foreign trade survey data collected by China’s Ministry of Commerce would provide reliable forecasts of China’s foreign trade. The research procedure is designed from three perspectives including forecast information test, turning point forecast, and out-of-sample value forecast. First, Granger causality test detects whether survey data lead exports and imports. Second, business cycle analysis, a non-model based method, is performed. The authors construct composite indexes with business survey data to forecast turning points of foreign trade. Third, model-based numerical forecasting methods, including the Autoregressive Integrated Moving Average Model with Exogenous Variables (ARIMAX) and the artificial neural networks (ANNs) models are estimated. Empirical results show that survey data granger cause imports and exports, the leading composite index provides signal for changes of trade cycles, and quantitative models including survey data generate more accurate forecasts than benchmark models. It is concluded that trade survey data has excellent predictive capabilities for imports and exports, which can offer some priorities for government policy-making and enterprise decision making.
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We are grateful to the Ministry of Commerce, People’s Republic of China for providing data.
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This paper was partially supported by the National Natural Science Foundation of China under Grant Nos. 71422015, 71988101, and the National Center for Mathematics and Interdisciplinary Sciences, Chinese Academy of Sciences.
This paper was recommended for publication by Editor FANG Ying.
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Bai, Y., Wang, S. & Zhang, X. Foreign Trade Survey Data: Do They Help in Forecasting Exports and Imports?. J Syst Sci Complex 35, 1839–1862 (2022). https://doi.org/10.1007/s11424-022-1015-x
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DOI: https://doi.org/10.1007/s11424-022-1015-x