计量实验报告模版.doc

计量经济学实验报告【实验名称】实验七、p132-134 例 4.2.1 自相关分析【实验目的】掌握自相关模型的检验方法、处理方法分析【知识准备】图形法检验、DW检验，拉格朗日乘数检验，广义差分法的运用【实验软件】 EVIEWS软件【实验要求】熟悉图形法检验、掌握DW检验、运用迭代法处理序列相关【实验方案与进度】【实验过程】 用普通最小二乘法建立模型：FileNewWorkfile在“Start date”中填“1978”，在“End date”中填“2006”， 单击“OK” 导入数据：FileImport Read Text-Lotus-Excel选择表2.6.3，单击“打开”在“Upper-left data cell”中填写“g3”,在“Names for series or Number of series if names in file”中填“x” 单击“OK” FileImport Read Text-Lotus-Excel选择表2.6.3，单击“打开”在“Upper-left data cell”中填写“h3”,在“Names for series or Number of series if names in file”中填“y” 单击“OK” 对导入的数据进行分析：QuickEstimate Equation填写“y空格c空格x”，单击“OK”，得出分析的结果。1. 自相关的检验(1)DW检验：根据上述的分析结果进行即可（2）图形法检验：生成新的序列：QuickGenerate Series填写“=resid”，单击“OK”。 作与t以及与的关系图：QuickGraph填写“t空格”,单击“OK” 选“Scatter diagram”,单击“OK”,得出图。 QuickGraph填写“ (-1)空格et”,单击“OK” 选“Scatter diagram”,单击“OK”,得出图。（3）拉格朗日检验：导入时间变量T: FileImport Read Text-Lotus-Excel选择表2.6.3，单击“打开”在“Upper-left data cell”中填写“i3”,在“Names for series or Number of series if names in file”中填“t” 单击“OK” 作y关于t平方n和x的回归：QuickEstimate Equation填写“y空格c空格x空格”，单击“OK”，得出分析的结果 含一阶滞后的回归：生成新的序列et,e1：QuickGenerate Series填写“et=resid”，单击“OK”, QuickGenerate Series填写“e1=et(-1)”，单击“OK”。 QuickEstimate Equation填写“et空格c空格x空格空格e1” 单击“OK”得出结果 含二阶滞后的回归：生成新的序列e2: QuickGenerate Series填写“e1=et(-2)”，单击“OK”。 QuickEstimate Equation填写“et空格c空格x空格空格e1空格e2”,单击“OK”得出结果2.自相关的处理：QuickEstimate Equation填写“y空格c空格x空格空格ar(1)”，单击“OK”得出结果3.序列相关稳健估计法 QuickEstimate EquationOption 选中“Heteroskedadticity”,单击“OK” 填写“y空格c空格x空格” ,单击“OK”，得出结果【实验小结】1.普通最小二乘法估计的结果Dependent Variable: YMethod: Least SquaresDate: 11/28/10 Time: 09:23Sample: 1978 2006Included observations: 29VariableCoefficientStd. Errort-StatisticProb. C2091.295334.98696.2429140.0000X0.4375270.00929747.059500.0000R-squared0.987955 Mean dependent var14855.72Adjusted R-squared0.987509 S.D. dependent var9472.076S.E. of regression1058.633 Akaike info criterion16.83382Sum squared resid30259014 Schwarz criterion16.92811Log likelihood-242.0903 F-statistic2214.596Durbin-Watson stat0.277155 Prob(F-statistic)0.000000建立消费函数： (6.24) (47.10) 2. 相关的检验（1）DW检验在5%的显著水平下，, (包括常数项)，,，所以存在自相关。 （2）图形法检验Et与t以及et与et-1的关系图由图可看出，随机项呈现正序列相关性。（3）拉格朗日检验关于和的回归Dependent Variable: YMethod: Least SquaresDate: 11/28/10 Time: 9:41Sample: 1978 2006Included observations: 29VariableCoefficientStd. Errort-StatisticProb. C3328.191195.032617.064790.0000X0.1761520.0259866.7787880.0000T221.655822.12418310.194890.0000R-squared0.997590 Mean dependent var14855.72Adjusted R-squared0.997404 S.D. dependent var9472.076S.E. of regression482.5729 Akaike info criterion15.29384Sum squared resid6054792. Schwarz criterion15.43528Log likelihood-218.7607 F-statistic5380.771Durbin-Watson stat0.442033 Prob(F-statistic)0.000000估计方程为： (17.06) (6.78) (10.19) 这里。D.W.值仍然较低，没有通过5%显著性水平下的D.W.检验，因此仍存在自相关。含一阶滞后残差项的辅助回归：Method: Least SquaresDate: 11/28/10 Time: 9:58Sample(adjusted): 1979 2006Included observations: 28 after adjusting endpointsVariableCoefficientStd. Errort-StatisticProb. C-47.00080122.0208-0.3851870.7035X0.0190910.0161921.1790790.2499T2-1.6186551.325944-1.2207560.2340E10.7606140.1223446.2170100.0000R-squared0.618910 Mean dependent var25.70108Adjusted R-squared0.571274 S.D. dependent var452.0910S.E. of regression296.0164 Akaike info criterion14.35027Sum squared resid2103016. Schwarz criterion14.54058Log likelihood-196.9038 F-statistic12.99243Durbin-Watson stat1.426529 Prob(F-statistic)0.000030回归方程为=-47.00+0.019X-1.62+0.761 (-0.39) (1.18) (-1.22) (6.22)R2=0.6189 ,在显著性水平为5%，自由度为一的情况下原模型存在序列相关性含二阶滞后残差项的辅助回归：Dependent Variable: EMethod: Least SquaresDate: 11/28/10 Time: 10:10Sample(adjusted): 1980 2006Included observations: 27 after adjusting endpointsVariableCoefficientStd. Errort-StatisticProb. C-61.20279120.4509-0.5081140.6164X0.0173600.0157341.1033630.2818T2-1.4202531.295108-1.0966290.2847E(-1)1.0563720.2000115.2815570.0000E(-2)-0.3628410.191018-1.8995100.0707R-squared0.656677 Mean dependent var44.12864Adjusted R-squared0.594255 S.D. dependent var449.8594S.E. of regression286.5520 Akaike info criterion14.31929Sum squared resid1806465. Schwarz criterion14.55926Log likelihood-188.3105 F-statistic10.51991Durbin-Watson stat1.994011 Prob(F-statistic)0.000064 回归方程为 = (-0.51) (1.10) (-1.10) (5.28) (-1.90),所以原模型存在序列相关性,但不存在二阶序列相关。1. 自相关的处理 Dependent Variable: YMethod: Least SquaresDate: 11/21/10 Time: 20:24Sample(adjusted): 1979 2006Included observations: 28 after adjusting endpointsConvergence achieved after 5 iterationsVariableCoefficientStd. Errort-StatisticProb. C3505.738403.30778.6924650.0000X0.1995350.0302636.5934070.0000TT219.242052.9267746.5744910.0000AR(1)0.7479230.1260425.9339060.0000R-squared0.999093 Mean dependent var15250.33Adjusted R-squared0.998979 S.D. dependent var9400.011S.E. of regression300.2877 Akaike info criterion14.37892Sum squared resid2164144. Schwarz criterion14.56924Log likelihood-197.3049 F-statistic8811.099Durbin-Watson stat1.394891 Prob(F-statistic)0.000000Inverted AR Roots .75估计方程为 (8.69) (6.59) (6.57) (5.93) ,DW检验不能判断模型是否存在序列相关性。,模型干扰项不存在自相关。为辅助回归辅助回归的结果：Dependent Variable: EEMethod: Least SquaresDate: 11/22/10 Time: 08:38Sample(adjusted): 1980 2006Included observations: 27 after adjusting endpointsVariableCoefficientStd. Errort-StatisticProb. C-7.02803296.02240-0.0731920.9423X-0.7480*X(-1)0.0103930.0293300.3543510.7263TT2-0.7480*(TT-1)2-0.8963932.791798-0.3210810.7510EE(-1)0.3150010.2068931.5225350.1415R-squared0.091596 Mean dependent var1.122090Adjusted R-squared-0.026891 S.D. dependent var288.4436S.E. of regression292.2962 Akaike info criterion14.32937Sum squared resid1965052. Schwarz criterion14.52134Log likelihood-189.4464 F-statistic0.773047Durbin-Watson stat1.882828 Prob(F-statistic)0.5209393.序列相关稳健估计法Dependent Variable: YMethod: Leas