# 消费意愿和能力对消费支出影响的实证分析 An Empirical Analysis of the Influence of Consumer Willingness and Ability on Consumer Expenditure

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Consumer spending is not only affected by the macroeconomic situation, but also closely related to consumers’ willingness to consume (confidence, expectations, etc.) and the ability to consume. In this paper, the consumer confidence index CCI and the consumer expectation index CEI are used as indicators to measure consumer confidence. The per capita disposable income DPI of urban residents is used as an indicator to measure consumption capacity, while the consumer expenditure is based on the per capita consumption expenditure NPE of urban residents. The VAR model was established by adopting co-integration analysis and impulse response methods, and empirically analyzed the influence of consumer confidence and consumption ability on consumer expenditure. The results show that CEI has a significant impact on NPE, and when analyzing the relationship between all variables, it is found that the contribution rate of DPI to NPE is the largest.

1. 引言

2. 模型选择与变量选取

Figure 1. Mechanism of “Conduction-Feedback”

(一) 指标与数据选取

Figure 2. Variation trend of each index after logarithm

(二) 模型设定

VAR (Vector Auto Regressive)模型由Sims (1980)提出，是当今世界上分析经济系统动态性的主流模型之一。VAR模型可以表述如下：

${Y}_{t}={\alpha }_{1}{Y}_{t-1}+{\alpha }_{2}{Y}_{t-2}+\cdots +{\alpha }_{p}{Y}_{t-p}+{\epsilon }_{t}$ (1)

3. 实证分析

(一) 稳定性检验及协整分析

Table 1. ADF testing process

Table 2. Cointegration testing process

(二) VAR模型及脉冲响应分析

1) AR根检验结果

Figure 3. AR root test results

2) VAR模型估计

$\begin{array}{l}\text{dlnnpe}=0.0\text{34}-0.\text{65}0\text{dlnnpe}\left(-\text{1}\right)-0.\text{382dlnnpe}\left(-\text{2}\right)\\ \text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\left(\text{5}.\text{359}\right)\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\left(-\text{8}.\text{871}\right)\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\left(-\text{5}.\text{224}\right)\\ \text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}+\text{1}.\text{183dlncei}-\text{1}.0\text{9}0\text{dlncci}+0.\text{445dlndpi}\\ \text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\left(\text{1}.\text{474}\right)\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\left(-\text{1}.\text{191}\right)\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\left(\text{1}0.\text{7}0\text{8}\right)\end{array}$ (2)

$\begin{array}{l}{Y}_{t}=\left[\begin{array}{cccc}-0.361& -0.282& 0.126& -0.502\\ -0.023& -0.667& 0.818& -0.050\\ -0.014& -0.796& 0.944& -0.038\\ 0.800& -0.379& 0.523& -1.129\end{array}\right]×{Y}_{t-1}\\ \text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}+\left[\begin{array}{cccc}0.278& 0.563& -0.824& -0.428\\ 0.089& 0.356& -0.762& -0.084\\ 0.069& 0.311& -0.637& -0.072\\ 1.340& 1.047& -1.098& -0.785\end{array}\right]×{Y}_{t-2}\\ \text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}+\left[\begin{array}{c}0.049\\ 0.004\\ 0.003\\ 0.026\end{array}\right]+{\epsilon }_{t}\end{array}$ (3)

(三) NPE对各经济指标的脉冲响应分析

Figure 4. Impulse response analysis

(四) 方差分解结果

Figure 5. Variance decomposition result of dlnnpe

4. 结论

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