乡村数字经济建设对重庆市农村居民收入的影响研究
A Study on the Impact of Rural Digital Economy Construction on the Incomes of Rural Residents in Chongqing
DOI: 10.12677/aam.2026.153123, PDF,   
作者: 尹鑫洁, 胡爱平:重庆理工大学数学科学学院,重庆;卜丹丹:立信会计师事务所(特殊普通合伙)浙江分所,浙江 杭州
关键词: 乡村数字经济主成分分析向量自回归模型门限向量自回归模型Rural Digital Economy Principal Component Analysis Vector Autoregressive Model Threshold Vector Autoregressive Model
摘要: 基于重庆市2011~2022年乡村数字经济与农村居民收入数据,运用主成分分析、向量自回归模型以及门限向量自回归模型,系统剖析乡村数字经济对农村居民收入的影响。首先利用主成分分析提取三个主成分,以此揭示乡村数字经济的多维结构特征,生成乡村数字经济指数。然后基于向量自回归模型分析结果,显示数字经济指数与农村居民收入的短期波动均存在随机性,并且两者滞后一阶的交互影响不大,数字经济对收入的短期驱动效应尚未得到充分释放。最后借助门限向量自回归模型识别出较大的门限效应:低区制下,农村居民收入增长依靠自身惯性,数字经济影响较小;高区制下,数字经济成为核心驱动力,农村居民收入模式从“惯性驱动”转变为“技术驱动”。
Abstract: Based on the data of rural digital economy and rural residents’ income in Chongqing from 2011 to 2022, principal component analysis, vector autoregressive model and threshold vector autoregressive model were used to systematically analyze the impact of rural digital economy on rural residents’ income. Firstly, principal component analysis is used to extract three principal components to reveal the multi-dimensional structural characteristics of rural digital economy and generate a rural digital economy index. Then, based on the analysis results of the vector autoregressive model, it shows that the short-term fluctuation of the digital economy index and rural residents’ income is random, and the interaction between the two is not large, and the short-term driving effect of the digital economy on income has not been fully released. Finally, the threshold vector autoregressive model is used to identify a large threshold effect: under the low-district system, the income growth of rural residents depends on their own inertia, and the impact of the digital economy is small. Under the high-district system, the digital economy has become the core driving force, and the income model of rural residents has changed from “inertia-driven” to “technology-driven”.
文章引用:尹鑫洁, 胡爱平, 卜丹丹. 乡村数字经济建设对重庆市农村居民收入的影响研究[J]. 应用数学进展, 2026, 15(3): 519-528. https://doi.org/10.12677/aam.2026.153123

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