中国社会消费品零售总额的相关问题研究

发布时间：2018-07-02 22:46

本文选题：主成分回归 + 灰色关联分析　；参考：《华中师范大学》2017年硕士论文

【摘要】：在当今世界,国家的发展离不开经济的发展,扩大消费需求是促进经济发展的重中之重,而社会消费品零售总额是反映一个国家人们消费水平的重要因素,在国民经济体系中,也是一个很重要的指标。因此,分析研究社会消费品零售总额对于处在转型期的中国经济更好的发展具有重要的意义。首先,本文以1991年至2015年近25年的时间序列数据为依据,以Eviews 8.0为工具,对取对数后的数据进行了主成分分析,根据累计贡献率,选取出几乎反映原变量所有信息的第一主成分F1t,再对选取出的主成分和社会消费品零售总额做主成分回归模型,最终得出主成分回归结果为:Yt=0.8308F1t+1.2145AR(1)-0.4614AR(2)。紧接着又用近 10 年的时间序列数据,运用 Matlab 编程进行了灰色关联度分析,得到如下结论:我国社会消费品零售总额与居民可支配收入、国内生产总值、居民消费水平有很大关系,而与人口数量、物价指数、恩格尔系数关系不大,最后在灰色关联度分析的基础上建立了灰色GM(1,5)模型,得出主要影响因素对社会消费品零售总额的影响水平。然后,本文分别以社会消费品零售总额1952年至2016年的年度数据以及2001年1月至2016年12月的月度数据为依据,根据自相关和偏自相关函数图进行模型识别,运用Eviews 8.0建立ARMA模型,并根据各种检验最终确定最优ARMA模型,最终在对社会消费品零售总额的年度数据预测中,建立了ARIMA(1,1,0)模型,并通过2011年至2016年六年的实际值,预测值之间的误差说明预测结果的理想性,接着再利用此模型对2017年至2020年未来4年的社会消费品零售总额进行预测。在对社会消费品零售总额的月度数据预测中,本文最终建立的是ARIMA(2,2,1)(0,1,1)12模型,同样通过各种检验说明所建模型为最优模型,并运用此模型对2017年的各月度社会消费品零售总额进行预测,进而再对预测结果进行分析。本文的研究结果对当今经济的发展具有一定的实际意义,有利于制定宏观经济政策,促进国家发展。
[Abstract]:In today's world, the development of the country is inseparable from the development of the economy. Expanding consumption demand is the most important thing in promoting economic development, and the total amount of retail sales of social consumer goods is an important factor reflecting the consumption level of people in a country, and in the national economic system, Is also a very important indicator. Therefore, it is of great significance to analyze and study the total retail sales of consumer goods for the better development of Chinese economy in the transition period. Firstly, based on the time series data of nearly 25 years from 1991 to 2015 and using Eviews 8.0 as a tool, this paper analyzes the logarithmic data by principal component analysis, according to the cumulative contribution rate. The first principal component F1t, which almost reflects all the information of the original variable, is selected, and then the principal component regression model is made for the selected principal component and the total retail sales of consumer goods. Finally, the result of principal component regression is: Ytt 0.8308F1t 1.2145AR (1) -0.4614AR (2). Then, with the time series data of nearly 10 years, the grey correlation degree analysis is carried out with Matlab programming, and the following conclusions are obtained: the total retail sales of consumer goods in China and the disposable income of the residents, the gross domestic product (GDP), There is a great relationship between the consumption level of residents, but not with the population quantity, price index, Engel coefficient. Finally, the grey GM (1 / 5) model is established on the basis of grey correlation degree analysis. The influence level of the main influencing factors on the total retail sales of consumer goods is obtained. Then, based on the annual data of total retail sales of consumer goods from 1952 to 2016 and the monthly data from January 2001 to December 2016, according to the autocorrelation and partial autocorrelation function diagrams, the ARMA model is established by using Eviews 8.0. According to various tests, the optimal ARMA model is determined. Finally, in the annual data forecast of the total retail sales of consumer goods, Arima model is established, and the actual value of six years from 2011 to 2016 is obtained. The error between the predicted values shows the ideal of the predicted results, and then uses this model to predict the total retail sales of consumer goods in the next four years from 2017 to 2020. In the monthly forecast of the total retail sales of consumer goods, the Arima (2 / 2 / 1) (0 / 1 / 1) 12 model is established in this paper. The model is also proved to be the optimal model through various tests. The model is used to forecast the monthly retail sales of consumer goods in 2017, and then the forecast results are analyzed. The results of this paper are of practical significance to the economic development of today, which is conducive to the formulation of macroeconomic policies and the promotion of national development.
【学位授予单位】：华中师范大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：F724

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