计量经济学实验报告3.doc

实 验 报 告程名称： 计量经济学 实验项目： 多元线性回归模型的估计和检验 实验类型：综合性 设计性 验证性专业班别： 09本国际经济与贸易3班 姓 名： 陈春丽 学 号： 409010304 实验课室： 创新楼E506 指导教师： 石立 实验日期： 2012年4月5日 广东商学院华商学院教务处 制 一、实验项目训练方案小组合作：是 否小组成员：陈春丽实验目的：掌握多元线性回归模型的估计和检验方法实验场地及仪器、设备和材料实验场地：创新楼E506使用EViews软件进行操作实验实验训练内容（包括实验原理和操作步骤）：（一）国内生产总值的增长模型分析广东省国内生产总值的增长，根据广东数据（数据见“表：广东省宏观经济数据-第三章.xls”文件，各变量的表示按照试验指导课本上的来表示）选择不变价GDP（GDPB）、不变价资本存量（ZC）和从业人员（RY），把GDPB作为因变量，ZC和RY作为两个解释变量进行二元线性回归分析。要求：按照试验指导课本，分别作：1、作散点图（GDPB同ZC，GDPB同RY） 2、进行因果关系检验（GDPB同ZC，GDPB同RY）Pairwise Granger Causality TestsDate: 04/05/12 Time: 14:30Sample: 1978 2005Lags: 2Null Hypothesis:ObsF-StatisticProbabilityGDPB does not Granger Cause ZC263.660560.04327ZC does not Granger Cause GDPB5.414570.01270Pairwise Granger Causality TestsDate: 04/05/12 Time: 14:30Sample: 1978 2005Lags: 3Null Hypothesis:ObsF-StatisticProbabilityGDPB does not Granger Cause RY252.008780.14880RY does not Granger Cause GDPB0.688050.571023、作GDPB同ZC和RY的多元线性回归，写出模型估计的结果，并分析模型检验是均否通过？（三个检验）Dependent Variable: GDPBMethod: Least SquaresDate: 04/05/12 Time: 14:40Sample: 1978 2005Included observations: 28VariableCoefficientStd. Errort-StatisticProb.RY-0.0578890.289863-0.1997130.8433ZC1.6691460.05664129.468680.0000C-476.1072771.3617-0.6172300.5427R-squared0.997221Mean dependent var5437.386Adjusted R-squared0.996998S.D. dependent var6304.032S.E. of regression345.3753Akaike info criterion14.62810Sum squared resid2982102.Schwarz criterion14.77083Log likelihood-201.7934F-statistic4485.175Durbin-Watson stat0.488314Prob(F-statistic)0.000000Dependent Variable: GDPBMethod: Least SquaresDate: 04/05/12 Time: 14:32Sample: 1978 2005Included observations: 28VariableCoefficientStd. Errort-StatisticProb.ZC1.6583880.01718496.510420.0000C-629.071689.74389-7.0096320.0000R-squared0.997216Mean dependent var5437.386Adjusted R-squared0.997109S.D. dependent var6304.032S.E. of regression338.9384Akaike info criterion14.55826Sum squared resid2986860.Schwarz criterion14.65342Log likelihood-201.8157F-statistic9314.262Durbin-Watson stat0.483741Prob(F-statistic)0.000000Dependent Variable: GDPBMethod: Least SquaresDate: 04/05/12 Time: 14:32Sample: 1978 2005Included observations: 28VariableCoefficientStd. Errort-StatisticProb.RY8.0655920.52526515.355270.0000C-21357.751786.464-11.955320.0000R-squared0.900682Mean dependent var5437.386Adjusted R-squared0.896862S.D. dependent var6304.032S.E. of regression2024.549Akaike info criterion18.13283Sum squared resid1.07E+08Schwarz criterion18.22799Log likelihood-251.8596F-statistic235.7844Durbin-Watson stat0.075138Prob(F-statistic)0.0000004、将建立的二元回归模型（GDPB同ZC和RY）同一元回归模型（GDPB同ZC、GDPB同RY）相比较，分析优点。答：估计方程的判定系数R-squared、参数显著性t检验、方程显著性F检验和调整的判定系数有些比一元回归有改进，表明这些确实应该进行二元回归。5、结合相关的经济理论，分析估计的二元回归模型的经济意义。二）宏观经济模型根据广东数据，研究广东省居民消费行为、固定资产投资行为、货物和服务净出口行为和存货行为，分别建立居民消费模型、固定资产投资模型、货物和服务净出口模型和存货增加模型。要求：按照试验指导课本，分别作出以下模型，并对需要改进的模型进行改进。写出最终估计的模型结果，并结合相关的经济理论，分析模型的经济意义。1、居民消费模型Pairwise Granger Causality TestsDate: 04/05/12 Time: 14:44Sample: 1978 2005Lags: 2Null Hypothesis:ObsF-StatisticProbabilityXFJ does not Granger Cause LB265.455160.01236LB does not Granger Cause XFJ7.190100.00418从散点图看它们之间具有线性关系，从因果关系检验看它们之间似乎具有双向因果关系，宏观经济中确实如此。进行医院线性回归如下：Dependent Variable: XFJMethod: Least SquaresDate: 04/05/12 Time: 14:46Sample: 1978 2005Included observations: 28VariableCoefficientStd. Errort-StatisticProb.LB0.9867020.01691658.330100.0000C-75.9966259.99073-1.2668060.2165R-squared0.992416Mean dependent var2362.277Adjusted R-squared0.992125S.D. dependent var2565.722S.E. of regression227.6909Akaike info criterion13.76260Sum squared resid1347921.Schwarz criterion13.85776Log likelihood-190.6765F-statistic3402.401Durbin-Watson stat0.701578Prob(F-statistic)0.000000得到回归方程：XFJ=0.986702392936*LB75.9966224788除劳动报酬LB外，企业盈余YY也会影响居民消费XFJ，看散点图和因果关系检验。Pairwise Granger Causality TestsDate: 04/05/12 Time: 14:53Sample: 1978 2005Lags: 1Null Hypothesis:ObsF-StatisticProbabilityXFJ does not Granger Cause YY270.093580.76232YY does not Granger Cause XFJ4.257200.05006显然回归得到了改善，引入YY是正确的，最后得到回归方程：XFJ=0.740808235483*LB+0.362075366798*YY+46.91513259832、固定资产投资模型Dependent Variable: TZGMethod: Least SquaresDate: 04/05/12 Time: 15:03Sample: 1978 2005Included observations: 28VariableCoefficientStd. Errort-StatisticProb.ZJ1.1760760.3208353.6656750.0012YY0.4375300.0521138.3958130.0000ZC0.0046540.0907970.0512540.9595C31.1967555.091700.5662700.5765R-squared0.997561Mean dependent var1628.997Adjusted R-squared0.997256S.D. dependent var2003.852S.E. of regression104.9672Akaike info criterion12.27674Sum squared resid264434.8Schwarz criterion12.46705Log likelihood-167.8743F-statistic3271.944Durbin-Watson stat1.369270Prob(F-statistic)0.000000Dependent Variable: TZGMethod: Least SquaresDate: 04/05/12 Time: 15:05Sample: 1978 2005Included observations: 28VariableCoefficientStd. Errort-StatisticProb.ZJ1.1918780.08699313.700910.0000YY0.4384220.0481299.1093650.0000C33.6561326.520921.2690410.2161R-squared0.997561Mean dependent var1628.997Adjusted R-squared0.997366S.D. dependent var2003.852S.E. of regression102.8521Akaike info criterion12.20542Sum squared resid264463.7Schwarz criterion12.34815Log likelihood-167.8758F-statistic5111.852Durbin-Watson stat1.370345Prob(F-statistic)0.000000Dependent Variable: TZGMethod: Least SquaresDate: 04/05/12 Time: 15:06Sample: 1978 2005Included observations: 28VariableCoefficientStd. Errort-StatisticProb.ZJ0.9959640.6223441.6003420.1221ZC0.2593370.1663771.5587260.1316C-162.319697.28026-1.6685770.1077R-squared0.990397Mean dependent var1628.997Adjusted R-squared0.989629S.D. dependent var2003.852S.E. of regression204.0684Akaike info criterion13.57574Sum squared resid1041098.Schwarz criterion13.71848Log likelihood-187.0604F-statistic1289.207Durbin-Watson stat0.415524Prob(F-statistic)0.000000Dependent Variable: TZGMethod: Least SquaresDate: 04/05/12 Time: 15:06Sample: 1978 2005Included observations: 28VariableCoefficientStd. Errort-StatisticProb.YY0.4247560.0636296.6755460.0000ZC0.3244900.03074610.553710.0000C-126.639342.05603-3.0112040.0059R-squared0.996195Mean dependent var1628.997Adjusted R-squared0.995891S.D. dependent var2003.852S.E. of regression128.4503Akaike info criterion12.64992Sum squared resid412487.1Schwarz criterion12.79265Log likelihood-174.0989F-statistic3272.948Durbin-Watson stat1.195612Prob(F-statistic)0.000000从上面三个回归可以结果看出，只要固定资产折旧ZJ和财政支出CZ其中一个不在方程中，回归就能得到很好的拟合。现在暂且取最后一个回归方程来使用。方程为TZG=0.430092510471*YY+1.8692783246*CZ+20.91892876333、货物和服务净流出模型Pairwise Granger Causality TestsDate: 04/05/12 Time: 15:11Sample: 1978 2005Lags: 2Null Hypothesis:ObsF-StatisticProbabilityGDPB does not Granger Cause CK267.440170.00361CK does not Granger Cause GDPB10.25630.00078从散点图和因果关系检验看它们具有关系，进行一元线性回归如下：Dependent Variable: CKMethod: Least SquaresDate: 04/05/12 Time: 15:13Sample: 1978 2005Included observations: 28VariableCoefficientStd. Errort-StatisticProb.GDPB0.0993160.00555217.888130.0000C-112.981345.74528-2.4697920.0204所有收集到的统计数据中，年利率LL是一个可以考虑引入的因素，引入LL进行二元线性回归如下：Dependent Variable: CKMethod: Least SquaresDate: 04/05/12 Time: 15:15Sample: 1978 2005Included observations: 28VariableCoefficientStd. Errort-StatisticProb.GDPB0.0882390.00552515.970670.0000LL-42.6598911.83064-3.6058800.0014C202.217395.250382.1230080.0438R-squared0.950564Mean dependent var427.0379Adjusted R-squared0.946609S.D. dependent var651.0303S.E. of regression150.4304Akaike info criterion12.96584Sum squared resid565732.7Schwarz criterion13.10857Log likelihood-178.5217F-statistic240.3512Durbin-Watson stat1.504205Prob(F-statistic)0.000000最后得到回归方程CK=0.0882381995057*GDP42.665702172*LL+202.2488405394、存货增加模型根据广东数据，建立存货增加TZC的二元回归模型如下：TZC=c+aCX+bPSL+u进行估计，结果为：Dependent Variable: TZCMethod: Least SquaresDate: 04/05/12 Time: 15:21Sample: 1978 2005Included observations: 28VariableCoefficientStd. Errort-StatisticProb.PSL1.7808060.1988598.9551120.0000CX0.0306330.0047396.4638880.0000C-209.054645.84519-4.5600130.0001R-squared0.952473Mean dependent var424.3629Adjusted R-squared0.948671S.D. dependent var3