虚拟变量eviews实验报告

计量经济学——金融硕士实验报告专业班级学号姓名成绩实验地点实验性质：演示性验证性综合性设计性实验项目名称虚拟解释变量回归一、实验目的：掌握虚拟解释变量回归模型的估计与应用，熟悉EViews的基本操作。二、实验内容及要求：个人所得税起征点调整对居民消费支出会产生重要的影响。为研究个人所得税起征点调整对城镇居民个人消费支出行为的效应，收集相关的数据如表3.1和表3.2所示。表3.1个人所得税起征点调整情况1987年1994年2006年2008年最低的起征点400元800元1600元2000元表3.2城镇居民收入与消费的有关数据城镇家庭平均每人可支配收入（元）城镇家庭平均每人全年消费性支出（元）平均每户城镇家庭就业人口数（人）城镇家庭平均每一就业者负担人数（含本人）（人）1985739.1673.22.151.811986900.97992.121.819871002.1884.42.091.7919881180.211042.031.7919891373.9121121.7819901510.161278.891.981.7719911700.61453.81.961.7519922026.61671.71.951.7319932577.42110.81.921.7219943496.22851.31.881.7419954282.953537.571.871.7319964838.93919.51.861.7219975160.34185.61.831.7419985425.14331.61.81.75199958544615.91.771.7720006279.9849981.681.8620016859.65309.011.651.8820027702.86029.921.581.9220038472.26510.941.581.9120049421.67182.11.561.912005104937942.881.511.96200611759.458696.551.531.93200713785.819997.471.541.89200815780.811242.91.481.97若模型设定为：Consumet=Ct+α1Incomet+α2Consumet-1+α3Employmentt+α4Burdent+α5d1t+α6d2t+α7d3t+α8d4t+εt其中Consumet表示t期城镇居民家庭人均消费支出，Incomet表示t期城镇居民家庭人均可支配收入，Employmentt表示t期城镇居民家庭平均每户就业人口，Burdent表示t期城镇居民家庭平均每一就业者负担人数，dit(i=1,2,3,4)相应的虚拟变量。构造用于描述个人所得税调整的虚拟变量，并简要说明其理由；考虑到个人所得税起征点调整对居民消费支出会产生重要的影响，而征税有m=5个不同阶段，故引入m-1=4个虚拟变量d1t，d2t，d3t，d4t。1985≤t<1987d1=0d2=0d3=0d4=01987≤t<1994d1=1d2=0d3=0d4=01994≤t<2006d1=1d2=1d3=0d4=02006≤t<2008d1=1d2=1d3=1d4=02008≤td1=1d2=1d3=1d4=1因为是个人所得税逐步增多的过程，所以在引入虚拟变量时，是使其在开始实施后依然保留之前的税收。用散点图描述两两变量之间的关系，并给出你对模型设定的结论；=1\*GB3①导入数据打开Eviews软件，选择“File”菜单中的“New--Workfile”选项（也可使用命令方式），建立工作文件后，录入数据。命令格式：DATAincomeconsumemploymentburdend1d2d3d4=2\*GB3②观测income、employment、burden同consum之间的关系，命令格式：SCATincomeconsumSCATemploymentconsumSCATburdenconsum根据相关关系图，可以认为，consum与income之间存在正相关的线性关系，consum与employment之间存在近似反比例的关系，consum与burden之间的关系图比较混乱，认为可能并不存在相关关系。依据测算，选择你认为更能描述客观实际的模型，并简要说明其理由；根据以上分析，可以得到如下模型：Consumet=Ct+α1Incomet+α2Consumet-1+α3log(Employmentt)+α5d1t+α6d2t+α7d3t+α8d4t+εt 在一定时期，技术固定，每户就业人口越多，劳动投入也就越多，相对的劳动力工资就会越低，消费也会降低，所以可以认为每户就业人口与消费存在着反向关系。从散点图也可以看出，两者的关系有些类似于对数函数。而消费与每一就业者负担人数之间的关系图比较混乱，认为可能并不存在相关关系，剔除该变量。作如下回归，命令格式：LSconsumCincomeconsum(-1)log(employment)d1d2d3d4DependentVariable:CONSUMMethod:LeastSquaresDate:04/19/14Time:11:50Sample(adjusted):19862008Includedobservations:23afteradjustingendpointsVariableCoefficientStd.Errort-StatisticProb.C744.7966378.06621.9700170.0676INCOME0.6331180.03519817.987290.0000CONSUM(-1)0.0848730.0509071.6672210.1162LOG(EMPLOYMENT)-762.9720478.5280-1.5944140.1317D137.4346050.234450.7451980.4677D2221.076538.308405.7709660.0000D3-122.049373.81439-1.6534610.1190D4-178.868865.87071-2.7154520.0160R-squared0.999861Meandependentvar4428.906AdjustedR-squared0.999796S.D.dependentvar3060.917S.E.ofregression43.70477Akaikeinfocriterion10.66100Sumsquaredresid28651.61Schwarzcriterion11.05595Loglikelihood-114.6015F-statistic15413.79Durbin-Watsonstat2.977604Prob(F-statistic)0.000000由上图可知，d1的伴随概率为0.4677>0.05，不显著，删除变量d1，再作如下回归，命令格式：LSconsumCincomeconsum(-1)log(employment)d2d3d4DependentVariable:CONSUMMethod:LeastSquaresDate:04/19/14Time:11:53Sample(adjusted):19862008Includedobservations:23afteradjustingendpointsVariableCoefficientStd.Errort-StatisticProb.C871.9310332.66272.6210670.0185INCOME0.6299220.03444718.286760.0000CONSUM(-1)0.0835760.0501651.6660170.1152LOG(EMPLOYMENT)-889.4616441.1508-2.0162300.0609D2226.036137.197916.0765790.0000D3-110.888471.26752-1.5559460.1393D4-171.692464.25105-2.6722110.0167R-squared0.999856Meandependentvar4428.906AdjustedR-squared0.999802S.D.dependentvar3060.917S.E.ofregression43.09316Akaikeinfocriterion10.61040Sumsquaredresid29712.33Schwarzcriterion10.95598Loglikelihood-115.0196F-statistic18496.74Durbin-Watsonstat2.787479Prob(F-statistic)0.000000由上图可知，d3的伴随概率为0.1393>0.05，不显著，删除变量d3，再作如下回归，命令格式：LSconsumCincomeconsum(-1)log(employment)d2d4DependentVariable:CONSUMMethod:LeastSquaresDate:04/19/14Time:11:56Sample(adjusted):19862008Includedobservations:23afteradjustingendpointsVariableCoefficientStd.Errort-StatisticProb.C1204.936265.10544.5451220.0003INCOME0.5991650.02936620.403200.0000CONSUM(-1)0.0993140.0511471.9417090.0689LOG(EMPLOYMENT)-1325.942354.4143-3.7412220.0016D2251.367534.815737.2199400.0000D4-141.771063.81647-2.2215430.0402R-squared0.999834Meandependentvar4428.906AdjustedR-squared0.999785S.D.dependentvar3060.917S.E.ofregression44.85802Akaikeinfocriterion10.66434Sumsquaredresid34208.12Schwarzcriterion10.96056Loglikelihood-116.6399F-statistic20483.46Durbin-Watsonstat2.638666Prob(F-statistic)0.000000由上图可知，consum(-1)的伴随概率为0.0689>0.05，不显著，删除变量consum(-1)，再作如下回归，命令格式：LSconsumCincomelog(employment)d2d4DependentVariable:CONSUMMethod:LeastSquaresDate:04/19/14Time:11:59Sample:19852008Includedobservations:24VariableCoefficientStd.Errort-StatisticProb.C1460.937233.29226.2622630.0000INCOME0.6531010.00913271.515390.0000LOG(EMPLOYMENT)-1651.937314.1501-5.2584310.0000D2277.404833.627838.2492610.0000D4-154.274266.05969-2.3353770.0306R-squared0.999810Meandependentvar4272.418AdjustedR-squared0.999769S.D.dependentvar3090.239S.E.ofregression46.92598Akaikeinfocriterion10.71807Sumsquaredresid41838.91Schwarzcriterion10.96350Loglikelihood-123.6169F-statistic24