计量论文我国居民消费水平影响因素分析

1、我国居民消费水平影响因素分析问题的提出分配和消费一直都是影响经济发展的两个重要因素，同时分配和消费又相互影响着对方。尤其在现在爆发全球性金融危机的情况下，我国的经济发展面临着国外需求萎缩，国内需求有限的艰难局面。因此如何扩大消费，促进经济又好又快发展成为当前经济工作的重点。本文主要通过分配和消费两者关系的论述来分析通过分配扩大消费促进经济的理论依据。模型的设定将“居民消费水平”设为因变量，“国内生产总值”“ 城镇居民人均收入”“ 农村居民人均收入”“ 人口自然增长率”和“居民消费价格指数”设为自变量，设定了以下计量经济学模型：其中Y=居民消费水平 (元)X1国内生产总值（亿元）X2城镇居民人均

2、收入（元）X3农村居民人均收入（元）X4人口自然增长率（）X5=居民消费价格指数参数估计居民消费水平y国内生产总值x1城镇居民人均收入x2农村居民人均收入x3人口自然增长率x4居民消费价格指数x5199083318667.81510.2686.314.39103.1199193221781.51700.6708.612.98103.41992111626923.52026.6784.011.60106.41993139335333.92577.4921.611.45114.71994183348197.93496.21221.011.21124.11995235560793.74283.015

3、77.710.55117.11996278971176.64838.91926.110.42108.31997300278973.05160.32090.110.06102.81998315984402.35425.12162.09.1499.21999334689677.15854.02210.38.1898.62000363299214.66280.02253.47.58100.420013887109655.26859.62366.46.95100.720024144120332.77702.82475.66.4599.220034475135822.88472.22622.26.011

4、01.220045032159878.39421.62936.45.87103.920055573184937.410493.03254.95.89101.820066263216314.411759.53587.05.28101.520077255265810.313785.84140.45.17104.820088349314045.415780.84760.65.08105.920099098340902.817174.75153.24.8799.320109968401202.019109.45919.04.79103.3注：以上数据来源于2011年中国统计年鉴根据1990年到2010

5、年数据进行OLS回归估计，结果如下：表OLS回归估计结果Dependent Variable: YMethod: Least SquaresDate: 11/13/11 Time: 17:24Sample: 1990 2009Included observations: 20VariableCoefficientStd. Errort-StatisticProb.  C693.1127222.12533.1203680.0075X10.0048590.0020042.4246230.0294X20.1917900.0574693.3372610.0049X30.732821

6、0.06362411.518020.0000X4-42.6126913.13034-3.2453610.0059X5-1.4466151.401104-1.0324820.3194R-squared0.999857    Mean dependent var3923.300Adjusted R-squared0.999806    S.D. dependent var2406.042S.E. of regression33.53005    Akaike info crite

7、rion10.10609Sum squared resid15739.70    Schwarz criterion10.40481Log likelihood-95.06086    Hannan-Quinn criter.10.16440F-statistic19564.08    Durbin-Watson stat1.621672Prob(F-statistic)0.000000Y =693.1127+0.004859X1+ 0.19179X2+0.732821X3

8、-42.61269X4 -1.446615X5 se(222.1253) (0.002004) (0.057469) (0.063624) (13.13034) (1.401104)t (3.120368) (2.424623) (3.337261) (11.51802) (-3.245361) (-1.032482)=0.999857 F = 19564.08 模型的检验及修正1 拟合优度：由表1中数据可以得到：=0.999857，=0.999806 说明模型对样本的拟合很好2 F检验：给定显著性水平=0.05，在F分布表中查得自由度为k-1=5和n-k=14的临界值为2.96，由上表中得到

9、F=19564.08，这说明回归方程显著，即“国内生产总值”“ 城镇居民人均收入”“ 农村居民人均收入”“ 人口自然增长率”和“居民消费价格指数”对“居民消费水平”整体影响显著。3 多重共线性由OLS回归结果（表一）可看见，该模型=0.999857，=0.999806可决系数高但是当= 0.05时，不仅X5、X6系数的符号与预期的相反，这表明很可能存在严重的多重共线性计算各解释变量的相关系数，如下：表2 相关系数矩阵X1X2X3X4X5X1 1.000000 0.995805 0.986195-0.854866-0.326879X2 0.995805&#

10、160;1.000000 0.993822-0.894457-0.349160X3 0.986195 0.993822 1.000000-0.898319-0.385662X4-0.854866-0.894457-0.898319 1.000000 0.441036X5-0.326879-0.349160-0.385662 0.441036 1.000000由相关系数矩阵可以看出，各解释变量之间的相关系数较高，证实确实存在严重多重共线性利用逐步回归修正的方法解决多重共线性问题分别做对X1、X2、X3、X4的一元回归

11、，结果如下：表3 一元回归结果变量X1X2X3X4X5参数估计值0.0249860.5214941.0856427-738.7562-133.3346t 统计值35.7102490.2984660.53392-8.819787-1.6907350.9860810.9977970.9951120.8120860.1370460.9853080.9976750.994840.8016470.089104其中加入X2的方程最大，以X2为基础，顺次加入其他变量逐步回归，结果如表所示：表加入新变量的回归结果（一）变量X1, X2X2, X3X2, X4X2, X50.9981390.9996880.99

12、80930.9983200.9979200.9996520.9978580.998122t-1.76651610.4948115.7997810.1562540.71831-1.62291493.35379-2.299627新加入X3后虽然=0.999652略有改进，而且各参数的t检验显著，应选择保留X3再加入其他新变量逐步回归，结果如表5所示表加入新变量的回归结果（二）变量X1, X2, X3X2, X4, X5X2，X3，X50.9997040.9984980.9997400.9996480.9982160.999692t0.9709956.5704296.55626117.8138410

13、.84669-2.41663616.431029.356161-1.790742X1,X4,X5虽然略有改进，但X1参数的t检验不显著，应该剔除；X2参数的t检验不显著，应予剔除X5情况与X4，X1相同，所以应予剔除最后修正多重共线性影响的回归结果为：表 修正多重共线性影响的OLS回归结果Dependent Variable: YMethod: Least SquaresDate: 11/13/11 Time: 18:47Sample: 1990 2009Included observations: 20VariableCoefficientStd. Errort-StatisticProb.

14、  C-121.410433.03292-3.6754350.0019X20.3182100.02014015.799780.0000X30.7291370.07179210.156250.0000R-squared0.999688    Mean dependent var3923.300Adjusted R-squared0.999652    S.D. dependent var2406.042S.E. of regression44.90517   

15、0;Akaike info criterion10.58446Sum squared resid34280.06    Schwarz criterion10.73382Log likelihood-102.8446    Hannan-Quinn criter.10.61362F-statistic27264.77    Durbin-Watson stat1.087943Prob(F-statistic)0.000000（33.03292) (0.020140) (0.0

16、71792) t (-3.675435) (15.79978) (10.15625) =0.999688 = 0.999652 F = 27264.774 异方差利用Eviews软件生成残差平方序列，绘制对X2和X3的散点如下： 图一图二由上图可以看出残差平方对解释变量X2，X3的散点图主要分步在图形中得下三角形，大致可以看出残差平方随X2，X3的变动呈增大的趋势，因此模型很可能存在异方差，但是是否确实存在还应通过更进一步的检验，下面将通过White检验法检验模型是否存在异方差利用Eviews软件对模型进行White检验，结果如下表：表White检验结果（一）Heteroskedasticit

17、y Test: WhiteF-statistic2.250346    Prob. F(5,14)0.1065Obs*R-squared8.911651    Prob. Chi-Square(5)0.1126Scaled explained SS11.73746    Prob. Chi-Square(5)0.0386Test Equation:Dependent Variable: RESID2Method: Least SquaresDate: 11/13/11 Tim

18、e: 19:05Sample: 1990 2009Included observations: 20VariableCoefficientStd. Errort-StatisticProb.  C-5490.13810870.52-0.5050480.6214X2-14.8846715.87532-0.9375980.3643X22-0.0100760.003958-2.5456230.0233X2*X30.0637000.0282922.2515370.0409X347.8673858.093580.8239700.4238X32-0.0998260.052076-1.9

19、169170.0759R-squared0.445583    Mean dependent var1714.003Adjusted R-squared0.247576    S.D. dependent var3357.793S.E. of regression2912.629    Akaike info criterion19.03482Sum squared resid1.19E+08    Schwarz criterion1

20、9.33354Log likelihood-184.3482    Hannan-Quinn criter.19.09314F-statistic2.250346    Durbin-Watson stat2.311088Prob(F-statistic)0.106465从表中可以看出，在下查分布表得临界值，所以模型不存在异方差 5 自相关对样本量为20，两个解释变量的模型，在显著水平下查DW统计表可知，由表6知，模型中DW为1.087943，而DW，说明存在自相关这一点也可以从残差图中看出。为解决自相关问题，采用

21、广义差分方法。Dependent Variable: YMethod: Least SquaresDate: 11/13/11 Time: 20:56Sample (adjusted): 1991 2009Included observations: 19 after adjustmentsConvergence achieved after 25 iterationsVariableCoefficientStd. Errort-StatisticProb.  C-105.551657.51138-1.8353170.0864X20.3196220.0328719.7235

22、320.0000X30.7197780.1174676.1275130.0000AR(1)0.4517060.2519421.7929010.0932R-squared0.999734    Mean dependent var4085.947Adjusted R-squared0.999681    S.D. dependent var2356.305S.E. of regression42.06442    Akaike info criterion10.50095Sum

23、 squared resid26541.23    Schwarz criterion10.69977Log likelihood-95.75898    Hannan-Quinn criter.10.53460F-statistic18822.15    Durbin-Watson stat1.749506Prob(F-statistic)0.000000Inverted AR Roots      .45由图可=

24、0.4517，对原模型进行广义差分，在Eviews命令栏中输入ls y-0.451706*y(-1) c x2-0.451706*x2(-1) x3-0.451706*x3(-1)，回车后可得方程输出结果Dependent Variable: Y-0.451706*Y(-1)Method: Least SquaresDate: 12/11/11 Time: 17:11Sample (adjusted): 1991 2009Included observations: 19 after adjustmentsVariableCoefficientStd. Errort-StatisticProb.  C-57.8713429.57589-1.9567070.0681