计量经济学实验报告三.doc

1序列相关实验：下表是1965年-1994年美国人均真实工资Y与人均产出指数X的数据，通过建立模型进行分析：年份YX196569.358.6196671.861196773.762.3196876.564.5196977.664.919707966.2197180.568.8197282.971197384.773.1197483.772.2197584.574.819768777.2197788.178.4197889.779.519799079.7198089.779.8198189.881.4198291.181.2198391.284198491.586.4198592.888.1198695.990.7198796.391.3198897.392.4198995.893.3199096.494.5199197.495.91992100100199399.9100.1199499.7101.4（1） 用普通最小二乘法估计模型参数Dependent Variable: YMethod: Least SquaresDate: 05/25/14 Time: 08:53Sample: 1965 1994Included observations: 30VariableCoefficientStd. Errort-StatisticProb. C33.429562.04155316.374570.0000X0.6801150.02509227.105040.0000R-squared0.963288 Mean dependent var88.12667Adjusted R-squared0.961976 S.D. dependent var8.691372S.E. of regression1.694788 Akaike info criterion3.957333Sum squared resid80.42455 Schwarz criterion4.050746Log likelihood-57.35999 F-statistic734.6830Durbin-Watson stat0.297779 Prob(F-statistic)0.000000Y=33.42956+0.680115x（2） 用图形法判断模型是否存在一阶自相关如图表明Ut存在一阶自相关，且大部分散点落在 一，三象限，则判为正相关（3） 用DW值检验模型是否存在一阶自相关对样本容量为30，一个解释变量的模型，在显著性水平5%下，查DW统计表可知DL=1.35 Du=1.49，模型中DWDu 所以模型扰动项已不存在一阶自相关2虚拟变量实验：下表给出了1965-1970年美国制造业利润和销售额的季度数据。假定利润不仅与销售额有关，而且和季度因素有关。要求对下列情况分别估计利润模型：利润销售额YX1965-I10503114862II12192123968III10834121454IV122011319171966-I12245129911III12213137828IV128201456451967-I11349136989II12715145126III11014141536IV127301517761968-I12539148826IIV149471684091969-I16049176057IIV143151833271970-I12381170415III12174176712IV10985180370（1）如果认为季度影响使利润平均值发生变异，应如何引入虚拟变量？哪个季度对利润平均值有显著影响（显著性水平为0.1）？你怎么得出这个结论的？并写出该季度的利润平均值。 用加法方式引入虚拟变量， Dependent Variable: YMethod: Least SquaresDate: 05/25/14 Time: 09:19Sample: 1965:1 1970:4Included observations: 24VariableCoefficientStd. Errort-StatisticProb. C6868.0151892.7663.6285590.0018X0.0382650.0114833.3322520.0035D1-182.1690654.3568-0.2783940.7837D21240.294630.68061.9665970.0640D3-400.3371636.1128-0.6293490.5366R-squared0.538662 Mean dependent var12863.54Adjusted R-squared0.441538 S.D. dependent var1453.439S.E. of regression1086.160 Akaike info criterion17.00174Sum squared resid22415107 Schwarz criterion17.24716Log likelihood-199.0208 F-statistic5.546131Durbin-Watson stat0.388380 Prob(F-statistic)0.003933在显著性水平为0.1的情况下，查表可得，自由度=n-k-1=24-4-1=19，t=1.7291.966597，所以在第二季度对利润水平有显著影响该季度的利润平均值为6868.015+1240.294=8108.309（2）如果认为季度影响同时使利润平均值和利润对销售额的变化率发生变异，应如何引入虚拟变量？3多重共线性实验：设被解释变量Y与解释变量X1、X2、X3、X4、X5、X6的数据如下：年份YX1X2X3X4X5X619887.459.12425.517.517.8185.8521.6819897.6059.32422.322.919.51185.3521.0819907.8559.3641823.718.93185.121.0319917.8059.2419.221.119.05184.820.7319926.99.86384.223.319.57184.621.9319937.478.7372.519.119.95184.2522.4919947.3859.46372.918.220.89181.3523.2619957.2259.88380.822.223.27179.324.3919968.1310401.727.626.06178.125.0419978.729.8406.528.828.55176.2525.5319989.14510.26410.527.830.12174.3526.64199910.1059.6244724.432.78174.2527.53200010.179.44452.824.132.21179.3528.12200110.5410.66467.127.833.57173.8531.35200210.63510.68495.219.534.86179.534.58200310.45512.3250025.436.6166.8541.78200410.99513.352528.440.35158.2542.85（1） 将Y关于其他变量线性回归，对方程进行检验，检验结果说明什么？Dependent Variable: YMethod: Least SquaresDate: 05/25/14 Time: 09:34Sample: 1988 2004Included observations: 17VariableCoefficientStd. Errort-StatisticProb. C4.5654296.2086410.7353350.4790X1-0.5556870.235955-2.3550530.0403X20.0154920.0027235.6900120.0002X30.0201770.0305780.6598630.5242X40.1436280.0352784.0713200.0022X5-0.0101730.027910-0.3645020.7231X60.0225920.0573770.3937430.7020R-squared0.985287 Mean dependent var8.740588Adjusted R-squared0.976459 S.D. dependent var1.437042S.E. of regression0.220485 Akaike info criterion0.106923Sum squared resid0.486134 Schwarz criterion0.450011Log likelihood6.091156 F-statistic111.6129Durbin-Watson stat2.181747 Prob(F-statistic)0.000000自由度为n-k-1=17-6-1=10，如果给定0.05的显著水平，显然X1，X3，X5，X6,不能通过，T检验，而且X1的系数的符号与预计不同，这表明很可能存在严重的多重共线性（2）对解释变量之间的相关系数进行检查，是否可怀疑自变量之间存在严重的多重共线性。X1X2X3X4X5X6X110.7794834535210.5231482324840.796702629133-0.9092500181750.932120653055X20.77948345352110.328326636080.813608255418-0.7030072876470.866638556141X30.5231482324840.3283266360810.58305308752-0.6524750926690.421143869829X40.7967026291330.8136082554180.583053087521-0.8831180413060.91260297194X5-0.909250018175-0.703007287647-0.652475092669-0.8831180413061-0.896653206367X60.9321206530550.8666385561410.4211438698290.91260297194-0.8966532063671由上表系数矩阵可以看出，X1和X5 X1和X6，X2和X4 X2和X6 X4和X2 X4和X5 X4和X6的相关系数都很高，证明确实存在较严重的多重共线性（4）简述逐步回归法的思路。对全部的自变量x1,x2,.,xp,按它们对Y贡献的大小进行比较，并通过F检验法，选择偏回归平方和显著的变量进入回归方程，每一步只引入一个变量，同时建立