计量经济学作业

3.2（1）用Eviews分析如下DependentVariable:YMethod:LeastSquaresDate:12/01/14Time:20:25Sample:19942011Includedobservations:18VariableCoefficientStd.Errort-StatisticProb.X20.1354740.01279910.584540.0000X318.853489.7761811.9285120.0729C-18231.588638.216-2.1105730.0520R-squared0.985838Meandependentvar6619.191AdjustedR-squared0.983950S.D.dependentvar5767.152S.E.ofregression730.6306Akaikeinfocriterion16.17670Sumsquaredresid8007316.Schwarzcriterion16.32510Loglikelihood-142.5903Hannan-Quinncriter.16.19717F-statistic522.0976Durbin-Watsonstat1.173432Prob(F-statistic)0.000000由表可知模型为：Y=0.135474X2+18.85348X3-18231.58检验：可决系数是0.985838，修正的可决系数为0.983950，说明模型对样本拟合较好。F检验，F=522.0976F（2,15）=4.77，回归方程显著。t检验，t统计量分别为X2的系数对应t值为10.58454，大于t（15）=2.131，系数是显著的，X3的系数对应t值为1.928512，小于t（15）=2.131，说明此系数是不显著的。（2）（2）表内数据ln后重新输入数据：DependentVariable:LNYMethod:LeastSquaresDate:10/25/15Time:22:18Sample:19942011Includedobservations:18VariableCoefficientStd.Errort-StatisticProb.C-10.810901.698653-6.3643970.0000LNX21.5737840.09154717.191060.0000X30.0024380.0009362.6053210.0199R-squared0.986373Meandependentvar8.400112AdjustedR-squared0.984556S.D.dependentvar0.941530S.E.ofregression0.117006Akaikeinfocriterion-1.302176Sumsquaredresid0.205355Schwarzcriterion-1.153780Loglikelihood14.71958Hannan-Quinncriter.-1.281714F-statistic542.8930Durbin-Watsonstat0.684080Prob(F-statistic)0.000000模型为lny=-10.81090+1.573784lnx2+0.002438x3检验：经济意义为其他条件不变的情况下，工业增加值每增加一个单位百分比出口货物总和增加1.57单位百分比，汇率每增加一单位百分比，出口总额增加0.0024个单位百分比。拟合优度检验，R^2=0.986373修正可决系数为0.984556，拟合很好。F检验对于H0:X2=X3=0，给定显著性水平a=0.05F(2,15)=4.77F=542.8930F(2,15)显著t检验对于H0:Xj=0（j=2,3），给定显著性水平a=0.05t（15）=2.131当j=2时tt(15)显著，当j=3时tt(15)显著。(3)两个模型表现出的汇率对Y的印象存在巨大差异3.3（1）用Eviews分析如下DependentVariable:YMethod:LeastSquaresDate:12/01/14Time:20:30Sample:118Includedobservations:18VariableCoefficientStd.Errort-StatisticProb.X0.0864500.0293632.9441860.0101T52.370315.20216710.067020.0000C-50.0163849.46026-1.0112440.3279R-squared0.951235Meandependentvar755.1222AdjustedR-squared0.944732S.D.dependentvar258.7206S.E.ofregression60.82273Akaikeinfocriterion11.20482Sumsquaredresid55491.07Schwarzcriterion11.35321Loglikelihood-97.84334Hannan-Quinncriter.11.22528F-statistic146.2974Durbin-Watsonstat2.605783Prob(F-statistic)0.000000由表可知模型为：Y=0.086450X+52.37031T-50.01638检验：可决系数是0.951235，修正的可决系数为0.944732，说明模型对样本拟合较好。F检验，F=539.7364F（2,15）=4.77，回归方程显著。t检验，t统计量分别为2.944186，10.06702，均大于t（15）=2.131，所以这些系数都是显著的。经济意义：家庭月平均收入增加1元，家庭书刊年消费支出增加0.086450元，户主受教育年数增加1年，家庭书刊年消费支出增加52.37031元。（2）用Eviews分析如下Y与T的一元回归DependentVariable:YMethod:LeastSquaresDate:12/01/14Time:22:30Sample:118Includedobservations:18VariableCoefficientStd.Errort-StatisticProb.T63.016764.54858113.854160.0000C-11.5817158.02290-0.1996060.8443R-squared0.923054Meandependentvar755.1222AdjustedR-squared0.918245S.D.dependentvar258.7206S.E.ofregression73.97565Akaikeinfocriterion11.54979Sumsquaredresid87558.36Schwarzcriterion11.64872Loglikelihood-101.9481Hannan-Quinncriter.11.56343F-statistic191.9377Durbin-Watsonstat2.134043Prob(F-statistic)0.000000模型：Y=63.01676T-11.58171X与T的一元回归DependentVariable:XMethod:LeastSquaresDate:12/01/14Time:22:34Sample:118Includedobservations:18VariableCoefficientStd.Errort-StatisticProb.T123.151631.841503.8676440.0014C444.5888406.17861.0945650.2899R-squared0.483182Meandependentvar1942.933AdjustedR-squared0.450881S.D.dependentvar698.8325S.E.ofregression517.8529Akaikeinfocriterion15.44170Sumsquaredresid4290746.Schwarzcriterion15.54063Loglikelihood-136.9753Hannan-Quinncriter.15.45534F-statistic14.95867Durbin-Watsonstat1.052251Prob(F-statistic)0.001364模型：X=123.1516T+444.5888（3）对残差模型进行分析，用Eviews分析如下DependentVariable:E1Method:LeastSquaresDate:12/03/14Time:20:39Sample:118Includedobservations:18VariableCoefficientStd.Errort-StatisticProb.E20.0864500.0284313.0407420.0078C3.96E-1413.880832.85E-151.0000R-squared0.366239Meandependentvar2.30E-14AdjustedR-squared0.326629S.D.dependentvar71.76693S.E.ofregression58.89136Akaikeinfocriterion11.09370Sumsquaredresid55491.07Schwarzcriterion11.19264Loglikelihood-97.84334Hannan-Quinncriter.11.10735F-statistic9.246111Durbin-Watsonstat2.605783Prob(F-statistic)0.007788模型：E1=0.086450E2+3.96e-14参数：斜率系数α为0.086450，截距为3.96e-14（4）由上可知，β2与α2的系数是一样的。回归系数与被解释变量的残差系数是一样的，它们的变化规律是一致的。3.4为了分析中国税收收入（Y）与国内生产总值（X2）、财政支出（X3）、商品零售价格指数（X4）的关系，利用1978~2007年的数据，用EViews作回归，部分结果如下：表3回归结果DependentVariable:LNYMethod:LeastSquaresDate:06/30/13Time:19:39Sample:19782007Includedobservations:30VariableCoefficientStd.Errort-StatisticProb.C-2.7553670.640080（1）0.0002LNX20.451234（2）3.1748310.0038LNX30.6271330.161566（3）0.0006X4（4）0.0056451.7955670.0842R-squared0.987591Meandependentvar8.341376AdjustedR-squared（5）S.D.dependentvar1.357225S.E.ofregression（6）Akaikeinfocriterion-0.707778Sumsquaredresid0.662904Schwarzcriterion-0.520952Loglikelihood14.61668F-statistic（7）Durbin-Watsonstat0.616136Prob(F-statistic)0.000000填补表中空缺数据：（1）tc==4.304723（2）==0.130789（3）==3.881590（4）==0.010136（5）===0.986159（6）S.Eofregression回归标准差===0.154783（7）===689.751148②分析回归结果：根据图中数据，模型估计的结果写为：=-2.755367+0.451234+0.627133+