Abstract
Correctly identifying causal relations among the industries provide accurate orientation to the dynamical industry connection, which is crucial to drive the industrial structure modification and optimization for a country. Since the non-separability, causality in industrial system has different formalization, in this paper, we exploit both dynamical and statistical measures driven by industry indexes to identify direct causation from indirect ones, which the variables are non-separable, weakly or moderately interacting. Partial cross mapping is employed to eliminate indirect causal influence among the 28 industries in China, further to explore real causal links for nonlinear industrial network system. The individual, local and overall linkage effects are measured out. Data experience shows that service-oriented industries are more active than before the epidemic of COVID-19, which brings benefits to the industries of communication, bank, textile and garment. Manufacturing industry is the central node of industrial chain network, which exhibits higher and stronger motivation under the background of COVID-19, and banking sector shows a persistent and strong influence on other industries at the post-epidemic era. The hot-industrial fields were figured out, and we would expect to provide quantitative references for the flow direction of industries and decision makings.
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YMD developed the causal-inference method, gave critical comments and contributed to manuscript writing. YLL wrote the manuscript, performed the computational analysis. All authors discussed the results and approved the manuscript.
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Yongmei, D., Yulian, L. Causal Linkage Effect on Chinese Industries via Partial Cross Mapping Under the Background of COVID-19. Comput Econ 63, 1071–1094 (2024). https://doi.org/10.1007/s10614-023-10408-0
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DOI: https://doi.org/10.1007/s10614-023-10408-0