新冠肺炎疫情期间湖北省农民经济收入分析

Bin Zhao, Jinming Cao
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摘要

本文探讨了新冠肺炎重大突发事件对湖北省农民经济收入影响的统计测度。选取湖北省作为分析对象,利用互联网收集了2013年一季度至2020年二季度湖北省农林牧渔业总产值和农民人均可支配收入五项数据。由于所有收集的数据都是宏观经济数据,因此将这些数据取对数以满足经济意义。以农民人均可支配收入为响应变量,通过因子分析得出影响农民收入的主要因素。畜牧业和渔业是湖北省的主要产业。然后以因子分析的得分作为解释变量,建立由影响因素组成的回归模型。本文采用多元线性回归、支持向量回归对数据进行拟合和预测,同时引入ARIMA模型的时间序列分析,通过AIC模型的选择,用2013年第一季度至2019年第二季度的拟合训练,反向预测两个季度和2019年三四个季度的实际数据进行比较,通过对序列图和评价指标模型的预测结果进行均方误差(RMSE)比较。三个模型预测了2020年第一季度和第二季度农民的人均可支配收入。研究发现,性能较好的ARIMA模型在模型中的表现比以前更差,并且三种预测值都高于模型的实际值,表明疫情对湖北省农业经济的影响是严重的。在此基础上,结合湖北省地貌气候特点,提出了建设性的建议。
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On farmers’ Economic Income in Hubei Province of China During the COVID-19 Epidemic
This paper discusses the statistical measurement of the impact of COVID-19 major emergencies on farmers’ economic income in Hubei Province. Hubei Province was selected as the object of analysis, and five data of total output value of agriculture, forestry, animal husbandry, fishery and per capita disposable income of farmers in Hubei Province from the first quarter of 2013 to the second quarter of 2020 were collected by using the Internet. Since all the collected data were macroeconomic data, these data were taken the logarithm to meet the economic significance. The per capita disposable income of farmers was taken as the response variable, and the main factors affecting farmers’ income were obtained by factor analysis. Livestock husbandry and fishery industries were the main industries in Hubei Province. Then the score of factor analysis were taken as explained variable to establish a regression model composed of influencing factors. This paper uses the multiple linear regression, support vector regression to fitting and forecasting data, ARIMA model of time series analysis, introduced at the same time, through the AIC model choice, with the first quarter of 2013 to 2019 in the second quarter fitting training, backward prediction two quarters, and three or four quarter of 2019 compared with the real data, through to the predicted results of the sequence diagram and evaluation index model to compare the mean square error (RMSE). Three models predict per capita disposable income of farmers in the first and second quarter of 2020. It has been found that performance better ARIMA model in the model compare is worse than before, and three kinds of predicted values are higher than the real value of the model, showed the outbreak to the influence of the agricultural economy in Hubei province is serious. On this basis, taking into account the characteristics of geomorphic climate in Hubei province, constructive suggestions are put forward.
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