私人汽车拥有量的计量经济学调查.doc

金融计量学论文我国私人汽车拥有量的分析班级：金融工程1003学号：0206100321姓名：梁梦莉成 绩1数据选取（20分）2模型建立与数据分析（40分）3Eviews应用（10分）4结论陈述（10分）5整体行文（20分）6总 分摘要随着我国经济的快速发展，私人汽车正逐步走入每个家庭，同时汽车的大幅增加势必对交通、能源和环境带来巨大的压力，这也制约着我国私人汽车的发展空间。本文根据近年来国内各项经济指标，运用计量经济学模型中的多元线性回归方法以及EVIEWS软件对我国私人汽车拥有量进行了科学的分析及预测，揭示出私人汽车拥有量的影响因素关键词：私人汽车拥有量 经济发展 影响因素AbstractWith the rapid development of our economy, private cars are gradually into every family, at the same time the car increase of traffic, the sources of energy and environment bring great pressure, which is also restricting our country private car development space. In this paper, according to the recent domestic economic indicators, using the econometrics model of multivariate linear regression method and EVIEWS software to our country private car has a quantity to undertake scientific analysis and forecast, revealing the private car has an amount of influence factorsKey words: Private car ownership Economic development Influence factors一、 理论背景汽车特别是用于消费的私人汽车拥有量的多少，与经济发展程度、居民收入以及道路建设等有着密切的联系。汽车作为中国家庭拥有率最低的一种高档耐用消费品，随着居民收入水平的不断提高和中国政府鼓励轿车进入家庭政策的出台，制约需求的各种不合理费用逐步取消和汽车贷款正在被越来越多人所接受，汽车正在快速进入普通家庭。然而，当我们快速迈进以私人汽车为主体的汽车社会的时候，也面临着新的考验。我国汽车社会面临能源紧缺、燃油价格上涨、土地资源有限等诸多不利因素。如果对这种快速增长不从战略的高度加以科学引导和调整，汽车的迅猛增长将不再单纯体现经济建设成就，巨大的负面效应也将成为社会发展的阻碍因素。在这样的背景下，进行私车发展转型刻不容缓，力图使私车保有量在节约、环保、节能的“框架”中适度增长。私人汽车保有量与一个国家或地区的社会经济发展的有关数据有着密切关系，同时也与我国交通状况有密切联系。因此我试图通过建立计量经济学模型来发现私人汽车保有量与有关社会经济数据之间的关系。二、 变量选取考虑各个数据的可获得性质，本文选取：被解释变量：Y，为私人汽车拥有量解释变量：，国内生产总值，人均可支配收入，汽车产量三、 数据采集19962011年各变量的统计数据年份私人汽车拥有量 （万辆）国内生产总值 （千亿元）人均可支配收入 （元）汽车产量(万辆)1996289.6771.184838.90147.521997358.3678.975160.30158.251998423.6584.405425.101631999533.8889.685854.00183.22000625.3399.216280.002072001770.78109.666859.60234.172002968.98120.337702.80325.120031219.23135.828472.20444.3920041481.66159.889421.60509.1120051848.07183.0810493.00570.4920062333.32210.8711759.5727.920072876.22249.5213785.8888.720084173.39300.6715780.8934.5520095314.31335.353188581382.6620106539.36397.983191091826.4720117872.12471.564239791841.89四、 实验分析1、建立多元线性回归模型Y=+利用eviews做ols分析，得Dependent Variable: YMethod: Least SquaresDate: 06/02/11 Time: 22:46Sample: 1996 2011Included observations: 16VariableCoefficientStd. Errort-StatisticProb. C-978.8384266.3186-3.6754410.0032X116.405517.7333262.1214040.0554X2-0.0575680.127273-0.4523160.6591X31.1823150.7480491.5805320.1400R-squared0.991097 Mean dependent var2351.771Adjusted R-squared0.988871 S.D. dependent var2384.876S.E. of regression251.5855 Akaike info criterion14.10576Sum squared resid759543.4 Schwarz criterion14.29891Log likelihood-108.8461 F-statistic445.2936Durbin-Watson stat0.848017 Prob(F-statistic)0.000000回归方程为Y=-978.8384+16.40551-0.057568-1.182315 (-3.67) (2.12) (-0.45) (1.58) =0.991097 =0.988871 F=445.2936通过对模型进行简单的分析可知，该模型的拟合程度非常好，且方程的显著程度也比较高。但是，有的解释变量的T值很小，说明很有可能存在多重共线性，下面进行检验与修正2、多重共线性检验由相关系数检验得YX1X2X3Y 1.000000 0.994272 0.987810 0.989534X1 0.994272 1.000000 0.995861 0.987529X2 0.987810 0.995861 1.000000 0.979317X3 0.989534 0.987529 0.979317 1.000000此模型具有高度的多重共线性（2）消除多重共线性第一步，将被解释变量Y对于每一解释变量建立一元回归模型Dependent Variable: YMethod: Least SquaresDate: 06/02/11 Time: 22:51Sample: 1996 2011Included observations: 16VariableCoefficientStd. Errort-StatisticProb. C-1349.940125.1413-10.787320.0000X119.116890.54921134.807880.0000R-squared0.988577 Mean dependent var2351.771Adjusted R-squared0.987761 S.D. dependent var2384.876S.E. of regression263.8390 Akaike info criterion14.10502Sum squared resid974554.4 Schwarz criterion14.20160Log likelihood-110.8402 F-statistic1211.589Durbin-Watson stat0.454970 Prob(F-statistic)0.000000R=0.988577Dependent Variable: YMethod: Least SquaresDate: 06/02/11 Time: 22:52Sample: 1996 2011Included observations: 16VariableCoefficientStd. Errort-StatisticProb. C-2011.199207.3502-9.6995270.0000X20.4017010.01691823.743680.0000R-squared0.975769 Mean dependent var2351.771Adjusted R-squared0.974038 S.D. dependent var2384.876S.E. of regression384.2703 Akaike info criterion14.85704Sum squared resid2067291. Schwarz criterion14.95361Log likelihood-116.8563 F-statistic563.7624Durbin-Watson stat0.989780 Prob(F-statistic)0.000000R=0.975769Dependent Variable: YMethod: Least SquaresDate: 06/02/11 Time: 22:53Sample: 1996 2011Included observations: 16VariableCoefficientStd. Errort-StatisticProb. C-348.2802137.8550-2.5264240.0242X34.0970390.15967625.658450.0000R-squared0.979178 Mean dependent var2351.771Adjusted R-squared0.977690 S.D. dependent var2384.876S.E. of regression356.2146 Akaike info criterion14.70541Sum squared resid1776444. Schwarz criterion14.80199Log likelihood-115.6433 F-statistic658.3561Durbin-Watson stat2.151209 Prob(F-statistic)0.000000R=0.979178由上可得，X1的相关性最强，故建立二元回归模型Dependent Variable: YMethod: Least SquaresDate: 06/02/11 Time: 22:55Sample: 1996 2011Included observations: 16VariableCoefficientStd. Errort-StatisticProb. C-1148.436257.4059-4.4615770.0006X124.557356.0851534.0356170.0014X2-0.1155460.128703-0.8977710.3856R-squared0.989244 Mean dependent var2351.771Adjusted R-squared0.987589 S.D. dependent var2384.876S.E. of regression265.6863 Akaike info criterion14.16987Sum squared resid917660.1 Schwarz criterion14.31473Log likelihood-110.3590 F-statistic597.8025Durbin-Watson stat0.749109 Prob(F-statistic)0.000000R=0.989244Dependent Variable: YMethod: Least SquaresDate: 06/02/11 Time: 22:56Sample: 1996 2011Included observations: 16VariableCoefficientStd. Errort-StatisticProb. C-1056.904196.5225-5.3780300.0001X113.247723.2230024.1103680.0012X31.2798350.6940451.8440230.0881R-squared0.990945 Mean dependent var2351.771Adjusted R-squared0.989552 S.D. dependent var2384.876S.E. of regression243.7674 Akaike info criterion13.99767Sum squared resid772492.9 Schwarz criterion14.14253Log likelihood-108.9813 F-statistic711.3632Durbin-Watson stat0.712958 Prob(F-statistic)0.000000R=0.990945由以上可以看出，X2为多余变量，删除修正后的回归方程为Y=-1056.904+13.24772+1.279835 3、异方差检验（1）判断是否产生异方差由G-Q检验，对样本按X1由大到小排序，去掉中间4个样本，剩余12个样本，再分成两个样本容量为6的子样本，对两个子样本分别用OLS法作回归。子样本1Dependent Variable: YMethod: Least SquaresDate: 06/02/11 Time: 23:02Sample: 1996 2001Included observations: 6VariableCoefficientStd. Errort-StatisticProb. C-2146.113383.9826-5.5890900.0113X118.210463.1735975.7381140.0105X30.8135000.6223211.3072030.2823R-squared0.994399 Mean dependent var4851.453Adjusted R-squared0.990665 S.D. dependent var2139.626S.E. of regression206.7224 Akaike info criterion13.80748Sum squared resid128202.5 Schwarz criterion13.70336Log likelihood-38.42245 F-statistic266.3186Durbin-Watson stat2.268106 Prob(F-statistic)0.000419子样本2Dependent Variable: YMethod: Least SquaresDate: 06/02/11 Time: 23:04Sample: 2006 2011Included observations: 6VariableCoefficientStd. Errort-StatisticProb. C-582.679863.13958-9.2284390.0027X17.9560273.5416762.2464020.1103X32.0641371.4930881.3824620.2608R-squared0.994398 Mean dependent var500.2783Adjusted R-squared0.990663 S.D. dependent var179.0568S.E. of regression17.30147 Akaike info criterion8.846313Sum squared resid898.0230 Schwarz criterion8.742193Log likelihood-23.53894 F-statistic266.2661Durbin-Watson stat2.509590 Prob(F-statistic)0.000419=128202.5=898.0230F=142.76拒绝同方差假设，表明模型存在异方差（2）异方差修正利用加权最小二乘法Dependent Variable: YMethod: Least SquaresDate: 06/03/11 Time: 08:34Sample: 1996 2011Included observations: 16Weighting series: 1/ABS(E1)VariableCoefficientStd. Errort-StatisticProb. C-1071.00748.81503-21.940110.0000X113.940940.71877519.395420.0000X31.1289460.1330448.4855250.0000Weighted StatisticsR-squared0.999942 Mean dependent var3155.020Adjusted R-squared0.999933 S.D. dependent var7955.197S.E. of regression65.00562 Akaike info criterion11.35419Sum squared resid54934.50 Schwarz criterion11.49905Log likelihood-87.83348 F-statistic42248.55Durbin-Watson stat0.440081 Prob(F-statistic)0.000000得到回归方程Y=-1071.007+13.94094+1.128946 从结果来看，拟合优度提高了，t统计量也有了改进。此时，模型已不存在异方差。4、自相关性检验由上已得出：DW=0.440081在=5%的显著性水平下，样本容量为16，DW的临界值=0.98，=1.54。因为DW的值介于0与之间，存在正相关自相关性修正第一步，赋值Dependent Variable: YMethod: Least SquaresDate: 06/03/11 Time: 08:42Sample(adjusted): 1997 2011Included observations: 15 after adjusting endpointsVariableCoefficientStd. Errort-StatisticProb. C-1089.908259.9372-4.1929680.0018X1-15.2468912.53187-1.2166490.2517X30.6530360.6583240.9919680.3446X1(-1)29.7518713.832702.1508360.0570X3(-1)1.7125510.8343522.0525530.0672R-squared0.994476 Mean dependent var2489.244Adjusted R-squared0.992266 S.D. dependent var2402.064S.E. of regression211.2457 Akaike info criterion13.80512Sum squared resid446247.5 Schwarz criterion14.04114Log likelihood-98.53842 F-statistic450.0446Durbin-Watson stat1.187505 Prob(F-statistic)0.000000第二步，设定新变量Dependent Variable: Y1Method: Least SquaresDate: 06/03/11 Time: 08:53Sample(adjusted): 1997 2011Included observations: 15 after adjusting endpointsVariableCoefficientStd. Errort-StatisticProb. C-356.797274.76592-4.7721910.0005X417.940601.72556810.396930.0000X50.5410710.3472512.5581530.1452R-squared0.973953 Mean dependent var939.9373Adjusted R-squared0.969612 S.D. dependent var896.2903S.E. of regression156.2426 Akaike info criterion13.11755Sum squared resid292940.9 Schwarz criterion13.25916Log likelihood-95.3