《计量经济学》实验报告样本QW.doc

安徽财经大学统计与数学模型分析实验中心计量经济学实验报告实验报告须双面打印。班级： 08数学 学号： 200872020 姓名：齐炜 实验时间 2011-4-18 实验地点 10-623 实验名称：实验1Eviews基本操作与ols估计 使用软件： Eviews实验目的通过本次实验的学习与上机操作，让同学们熟悉Eviews的工作界面，掌握Eviews的最基本操作方法；学会在Eviews中采用OLS方法估计模型参数。实验内容习题2.14（1）结果显示：Dependent Variable: YMethod: Least SquaresDate: 04/18/11 Time: 07:28Sample: 1988 1998Included observations: 11VariableCoefficientStd. Errort-StatisticProb. C158.5398121.80711.3015640.2293X10.0494040.00468410.547860.0000X2-0.9116840.989546-0.9213160.3838R-squared0.947989 Mean dependent var190.4827Adjusted R-squared0.934986 S.D. dependent var79.29127S.E. of regression20.21757 Akaike info criterion9.077982Sum squared resid3270.001 Schwarz criterion9.186499Log likelihood-46.92890 F-statistic72.90647Durbin-Watson stat1.035840 Prob(F-statistic)0.000007二次函数模型计算结果：Estimation Command:=LS Y C X1 X2Estimation Equation:=Y = C(1) + C(2)*X1 + C(3)*X2Substituted Coefficients:=Y = 158.5398355 + 0.04940379666*X1 - 0.911684216*X2(2)二次函数残差分析：obsActualFittedResidualResidual Plot1988137.161119734207938| . | . *|1989124.56104.93154838219.6284516178| . | * |1990107.91115.817781734-7.90778173362| . * | . |1991102.96128.732157754-25.7721577541|* . | . |1992125.24146.989370216-21.7493702162| *. | . |1993162.45167.481868747-5.03186874685| . * | . |1994217.43204.06720759513.3627924048| . | * . |1995253.42242.09936533211.3206346684| . | * . |1996251.07270.766359062-19.6963590621| * | . |1997285.85291.905157236-6.05515723606| . * | . |1998327.26311.33260473615.9273952642| . | * . |(3)结果显示：Dependent Variable: Y/X2Method: Least SquaresDate: 04/18/11 Time: 07:42Sample: 1988 1998Included observations: 11VariableCoefficientStd. Errort-StatisticProb. C2.6836770.9970442.6916330.0274X10.0003833.83E-059.9864320.0000X2-0.0184260.008100-2.2748860.0525R-squared0.934858 Mean dependent var1.450649Adjusted R-squared0.918573 S.D. dependent var0.579944S.E. of regression0.165490 Akaike info criterion-0.532816Sum squared resid0.219094 Schwarz criterion-0.424300Log likelihood5.930491 F-statistic57.40468Durbin-Watson stat1.086150 Prob(F-statistic)0.000018二次函数模型计算结果：Estimation Command:=LS Y/X2 C X1 X2Estimation Equation:=Y/X2 = C(1) + C(2)*X1 + C(3)*X2Substituted Coefficients:=Y/X2 = 2.683676989 + 0.0003828671453*X1 - 0.01842626179*X2习题2.16（1）线性化方法：双对数模型结果显示：Dependent Variable: LOG(Y)Method: Least SquaresDate: 04/18/11 Time: 08:12Sample(adjusted): 1981 1996Included observations: 15Excluded observations: 1 after adjusting endpointsVariableCoefficientStd. Errort-StatisticProb. C-6.5792442.517260-2.6136530.0241T0.0171500.0107561.5945070.1391LOG(L)0.8851830.3984952.2213140.0483LOG(K)0.9574860.06213515.409750.0000R-squared0.998685 Mean dependent var5.535905Adjusted R-squared0.998326 S.D. dependent var0.695611S.E. of regression0.028457 Akaike info criterion-4.057662Sum squared resid0.008908 Schwarz criterion-3.868848Log likelihood34.43246 F-statistic2784.761Durbin-Watson stat1.588874 Prob(F-statistic)0.000000双对数模型：Estimation Command:=LS LOG(Y) C T LOG( L) LOG( K)Estimation Equation:=LOG(Y) = C(1) + C(2)*T + C(3)*LOG(L) + C(4)*LOG(K)Substituted Coefficients:=LOG(Y) = -6.579243906 + 0.01715045626*T + 0.8851829088*LOG(L) + 0.9574859655*LOG(K)迭代法：结果显示：Dependent Variable: YMethod: Least SquaresDate: 04/18/11 Time: 08:26Sample(adjusted): 1981 1996Included observations: 15Excluded observations: 1 after adjusting endpointsConvergence achieved after 8 iterationsY=C(1)*LC(2)*KC(3)*C(4)TCoefficientStd. Errort-StatisticProb. C(1)287.89521442.0250.1996460.8454C(2)-1.1500990.843275-1.3638480.1999C(3)0.9567740.07460312.824820.0000C(4)1.0476690.01644963.691430.0000R-squared0.997537 Mean dependent var320.1593Adjusted R-squared0.996865 S.D. dependent var240.9549S.E. of regression13.49143 Akaike info criterion8.265165Sum squared resid2002.206 Schwarz criterion8.453978Log likelihood-57.98874 Durbin-Watson stat1.132297Estimation Command:=LS Y=C(1)*LC(2)*KC(3)*C(4)TEstimation Equation:=Y=C(1)*LC(2)*KC(3)*C(4)TSubstituted Coefficients:=Y=287.8952148*L-1.15009855*K0.9567738211*1.04766876T实验结果分析习题2.14结果分析：（1）Y = 158.5398355 + 0.04940379666*X1 - 0.911684216*X2（3）Y/X2 = 2.68367