计量经济学实验报告——课程论文.doc

本文档系作者精心整理编辑，实用价值高。课程名称 计 量 经 济 学 论文题目 全国粮食产量因素分析综合报告 摘 要： （关于论文的150字左右的概括性介绍）计量经济学是一种使用在经济领域的方法，通过一个学期的实践操作和学习，进行一个总的课程论文的报告。本次报告研究了粮食产量和化肥用量、农机动力、劳动力、粮食播种面积之间的关系，通过数据的搜集，模型的建立和假设，回归分析，经济学检验（包括多重共线性检验、异方差检验、序列相关检验），最后得出总的模型和结论。通过这次的研究和实验，能更加了解贯通这门学科的思想和科学方法，在运用软件的基础上更加理解主要内容。关键词：（3-5个关键词）粮食产量化肥用量 农机动力 劳动力引言：关于选题意义、背景的介绍，明确研究对象（Y）,和相应的解释变量X1、X2、先X3、X4(至少3个解释变量)等。在中国经济快速发展的同时，农业经济的发展也是至光重大的，而粮食产量则代表着一个国家农业发展的现代化水平和技术水平，因此通过研究我国19832007年的全国粮食总产量和其他变量之间的关系，可以了解对粮食产量产生影响的因素，为了更合理规划农业产量，研究和粮食产量相关系的因素，通过计量经济学的方法，科学合理地解释其中之间的关系。研究对象是中国19832007年的全国粮食总产量（Y）。通过仔细考虑，选择的解释变量：农业化肥施用量（X1）为 ，农机机械总动力（X2）和农业劳动力（X3）粮食播种面积（X4）。肥和农机动力是影响产量的重大因素，而劳动力对产量起着非常重要的作用，粮食播种面积也可能和粮食总产量有一定关系。理论假设：包含相关的理论观点和理论假设，确定被解释变量与解释变量之间的关系，建立理论模型。并提出本文的理论假。从基本理论判断，可以知道粮食总产量随着农业化肥施用量、农机机械总动力的使用量的增多，增加，因此之间呈正相关关系。劳动力使用的越多，粮食播种面积的增加，相应的农业总产值也可能越高，因此它们也成正相关关系。建立模型为：1、 数据收集和整理对收集数据的过程和数据的简单处理过程等做详细客观的说明，列出数据列表，注明数据出处。数据选自数据库中中国粮食产量和相关因素表年份总粮食产量(Y)(万吨)农业化肥施用量(X1)（万公斤）农业机械总动力(X2)（万千瓦）农业劳动力(X3)（万人）粮食播种面积(X4)（千公顷）19833872816601802231151114047198440731174019497308681128841985379111776209133113010884519863915119312295031254110933198740208199924836316631112681988394082142265753224911012319894075523572806733225112205199044624259028708389141134661991435292806293893909811231419924426429303030838699110560199345649315231817376801105091994445103318338023662810954419954666235943611835530110060199650454382838547348201125481997494173981420163484011291219985123040844520835177113787199950839412448996357681131612000462184146525743604310846320014526442545517236513106080200245706433957930368701038912003430704412603873654699410200446947463764028352691016062005484024766683983397010427820064980449287252232561104958200750160510876590314441056382、 回归及回归结果分析包括回归结果的输出和解读，做经济学检验， R2 检验、F检验和t 检验，确定初步的模型。用Evies软件进行回归，结果为:Dependent Variable: YMethod: Least SquaresDate: 12/15/11 Time: 16:34Sample: 1983 2007Included observations: 25VariableCoefficientStd. Errort-StatisticProb. C-31609.9410494.78-3.0119670.0069X15.4015630.8414846.4190900.0000X2-0.0600550.058633-1.0242640.3179X3-0.0611710.098197-0.6229340.5404X40.5751100.0808557.1128280.0000R-squared0.959739 Mean dependent var44945.64Adjusted R-squared0.951687 S.D. dependent var4150.729S.E. of regression912.3388 Akaike info criterion16.64676Sum squared resid16647241 Schwarz criterion16.89053Log likelihood-203.0845 F-statistic119.1903Durbin-Watson stat2.024219 Prob(F-statistic)0.0000001、 模型估计结果：t-Statistic (-3.011967) (6.419090) (-1.024264) (-0.622934) （7.112828）R-squared =0.959739 F=119.1903 D.W. = 2.024219 N=25 k=4 2、经济检验：从回归结果看，在保持其他条件不变的条件下，农业化肥施用量每增加一个单位，粮食总产量将增加个单位；农业机械总动力每增加一个单位，粮食总产量将减少0.060055个单位；农业劳动力每增加一个单位，粮食总产量将减少0.061171个单位。，粮食播种面积每增加一个单位，粮食总产量将增加0.575110个单位。和预测的理论假设结论有一些出入。统计检验R2 检验、F检验和t 检验R2 检验拟合优度：由=0.959739可知，农业总产值可以用上述变量解释的比例约 96%，拟合优度较高。F检验：在显著水平为0.05时，在F分布表上查自由度为k-1=3，n-k=21的临界值F（3，21）=8.62，很明显F=119.1903大于8.62，所以所有变量联合起来对模型由显著影响。T检验：再显著条件为0.05的情况下,查自由度为8的t分布表此时，t（8）=2.076可见，x2，x3,的t检验不显著，而x1，x4是显著的，说明可能存在多重共线性问题。2、 计量经济学检验及模型的修正对模型进行经济意义及残差图的计量经济学检验，检验是否存在违背经典假设的问题。检验步骤为：51 多重共线性检验，确定最终的解释变量。多重共线性检验：系数显著否？参数符号正确否? 利用相关系数矩阵法、逐步回归法进行检验；利用逐步回归法进行修正。相关系数矩阵法：从图中可以看出X1和X2相关系数为0.952746，可以判断这个模型中部分变量之间有明显的线性关系，整个模型的多重共线性比较高。YX1X2X3X4Y 1.000000 0.849331 0.705571 0.351232-0.140987X1 0.849331 1.000000 0.952746 0.314885-0.616566X2 0.705571 0.952746 1.000000 0.128834-0.741538X3 0.351232 0.314885 0.128834 1.000000-0.060970X4-0.140987-0.616566-0.741538-0.060970 1.000000逐步回归法:第一步：将因变量Y分别对X1、X2、X3、X4作简单的一元回归。Dependent Variable: YMethod: Least SquaresDate: 12/15/11 Time: 17:11Sample: 1983 2007Included observations: 25VariableCoefficientStd. Errort-StatisticProb. C34256.141455.79923.530810.0000X13.1587610.4093507.7165250.0000R-squared0.721363 Mean dependent var44945.64Adjusted R-squared0.709249 S.D. dependent var4150.729S.E. of regression2238.130 Akaike info criterion18.34129Sum squared resid1.15E+08 Schwarz criterion18.43880Log likelihood-227.2661 F-statistic59.54476Durbin-Watson stat0.789143 Prob(F-statistic)0.000000Dependent Variable: YMethod: Least SquaresDate: 12/15/11 Time: 18:26Sample: 1983 2007Included observations: 25VariableCoefficientStd. Errort-StatisticProb. C38116.351551.31624.570340.0000X20.1652190.0346004.7750660.0001R-squared0.497830 Mean dependent var44945.64Adjusted R-squared0.475997 S.D. dependent var4150.729S.E. of regression3004.632 Akaike info criterion18.93032Sum squared resid2.08E+08 Schwarz criterion19.02783Log likelihood-234.6290 F-statistic22.80125Durbin-Watson stat0.509117 Prob(F-statistic)0.000082Dependent Variable: YMethod: Least SquaresDate: 12/15/11 Time: 18:27Sample: 1983 2007Included observations: 25VariableCoefficientStd. Errort-StatisticProb. C25719.7810716.002.4001300.0249X30.5537970.3078241.7990710.0851R-squared0.123364 Mean dependent var44945.64Adjusted R-squared0.085249 S.D. dependent var4150.729S.E. of regression3969.865 Akaike info criterion19.48747Sum squared resid3.62E+08 Schwarz criterion19.58498Log likelihood-241.5934 F-statistic3.236658Durbin-Watson stat0.318936 Prob(F-statistic)0.085144Dependent Variable: YMethod: Least SquaresDate: 12/15/11 Time: 18:27Sample: 1983 2007Included observations: 25VariableCoefficientStd. Errort-StatisticProb. C60721.9623114.792.6269740.0151X4-0.1442870.211264-0.6829710.5014R-squared0.019877 Mean dependent var44945.64Adjusted R-squared-0.022737 S.D. dependent var4150.729S.E. of regression4197.651 Akaike info criterion19.59906Sum squared resid4.05E+08 Schwarz criterion19.69657Log likelihoodDurbin-Watson stat-242.9882 F-statistic0.4664500.333336 Prob(F-statistic)0.501447第二步、选择最优方程：用农业总产量Y大一所有解释变量作用的回归中，我们发现农业总产量Y和X1农业化肥施用量的拟合优度最高，所以选择X1的变量建立模型： 为最优的基本回归方程。Eviews输出结果如图。对各个变量进行回归一、x1变量的回归Dependent Variable: YMethod: Least SquaresDate: 12/15/11 Time: 17:11Sample: 1983 2007Included observations: 25VariableCoefficientStd. Errort-StatisticProb. C34256.141455.79923.530810.0000X13.1587610.4093507.7165250.0000R-squared0.721363 Mean dependent var44945.64Adjusted R-squared0.709249 S.D. dependent var4150.729S.E. of regression2238.130 Akaike info criterion18.34129Sum squared resid1.15E+08 Schwarz criterion18.43880Log likelihood-227.2661 F-statistic59.54476Durbin-Watson stat0.789143 Prob(F-statistic)0.000000 R-squared=0.721363 X1的t-Statistic=7.716525，比较显著。二、将X2加入中，输出结果如图Dependent Variable: YMethod: Least SquaresDate: 12/15/11 Time: 18:32Sample: 1983 2007Included observations: 25VariableCoefficientStd. Errort-StatisticProb. C31659.781310.54924.157640.0000X17.1380201.0514706.7886120.0000X2-0.2629690.066203-3.9721740.0006R-squared0.837737 Mean dependent var44945.64Adjusted R-squared0.822986 S.D. dependent var4150.729S.E. of regression1746.340 Akaike info criterion17.88060Sum squared resid67093443 Schwarz criterion18.02686Log likelihood-220.5075 F-statistic56.79109Durbin-Watson stat1.193338 Prob(F-statistic)0.000000整体拟合优度提到到0.837737,t值也非常显著，保留X2三、加入X3变量，进行回归。Dependent Variable: YMethod: Least SquaresDate: 12/15/11 Time: 18:34Sample: 1983 2007Included observations: 25VariableCoefficientStd. Errort-StatisticProb. C40301.455161.8577.8075480.0000X18.5776201.3076906.5593700.0000X2-0.3437180.078803-4.3617220.0003X3-0.2931080.169824-1.7259550.0990R-squared0.857895 Mean dependent var44945.64Adjusted R-squared0.837594 S.D. dependent var4150.729S.E. of regression1672.728 Akaike info criterion17.82795Sum squared resid58758374 Schwarz criterion18.02297Log likelihood-218.8493 F-statistic42.25930Durbin-Watson stat1.406864 Prob(F-statistic)0.000000整体拟合优度从0.837737提到到0.857895，并不是很多，但是t值不显著X3的P值为0.0990 并且影响了其他变量，因此排除X3。四、加入X4变量Dependent Variable: YMethod: Least SquaresDate: 12/15/11 Time: 18:40Sample: 1983 2007Included observations: 25VariableCoefficientStd. Errort-StatisticProb. C-35305.908529.622-4.1392100.0005X15.0418870.6031558.3591910.0000X2-0.0368120.044564-0.8260480.4181X40.5918350.0751487.8756270.0000R-squared0.958958 Mean dependent var44945.64Adjusted R-squared0.953095 S.D. dependent var4150.729S.E. of regression898.9475 Akaike info criterion16.58597Sum squared resid16970237 Schwarz criterion16.78099Log likelihood-203.3247 F-statistic163.5572Durbin-Watson stat1.923216 Prob(F-statistic)0.000000整体拟合优度从0.837737提到到0.958958，且回归系数在经济理论上和统计检验上合格，新变量是有利变量，予以接纳。最后的回归方程为：5.2 异方差检验，并对异方差进行修正。异方差检验（重点针对横截面数据），具体用残差图法Dependent Variable: YMethod: Least SquaresDate: 12/15/11 Time: 18:54Sample: 1983 2007Included observations: 25VariableCoefficientStd. Errort-StatisticProb. C-35305.908529.622-4.1392100.0005X15.0418870.6031558.3591910.0000X2-0.0368120.044564-0.8260480.4181X40.5918350.0751487.8756270.0000R-squared0.958958 Mean dependent var44945.64Adjusted R-squared0.953095 S.D. dependent var4150.729S.E. of regression898.9475 Akaike info criterion16.58597Sum squared resid16970237 Schwarz criterion16.78099Log likelihood-203.3247 F-statistic163.5572Durbin-Watson stat1.923216 Prob(F-statistic)0.000000在显著性为0.05情况下，t=-4.139210统计检验不显著，因此去掉常数项，重新做模型的回归。Dependent Variable: YMethod: Least SquaresDate: 12/15/11 Time: 18:58Sample: 1983 2007Included observations: 25VariableCoefficientStd. Errort-StatisticProb. X16.0115120.7317518.2152450.0000X2-0.1497370.046392-3.2276600.0039X40.2817570.00782536.007340.0000R-squared0.925474 Mean dependent var44945.64Adjusted R-squared0.918698 S.D. dependent var4150.729S.E. of regression1183.515 Akaike info criterion17.10253Sum squared resid30815577 Schwarz criterion17.24880Log likelihood-210.7817 Durbin-Watson stat1.580405估计方程为初步感觉存在异方差park检验Dependent Variable: LOG(RESID2)Method: Least SquaresDate: 12/15/11 Time: 19:12Sample: 1983 2007Included observations: 25VariableCoefficientStd. Errort-StatisticProb. LOG(X1)-6.9981486.284660-1.1135290.2781LOG(X2)7.8997786.0800431.2992960.2079LOG(X4)23.8823122.498611.0615020.3005C-291.0843277.4084-1.0492990.3060R-squared0.085197 Mean dependent var12.79859Adjusted R-squared-0.045489 S.D. dependent var2.437385S.E. of regression2.492206 Akaike info criterion4.809860Sum squared resid130.4329 Schwarz criterion5.004880Log likelihood-56.12325 F-statistic0.651920Durbin-Watson stat2.322507 Prob(F-statistic)0.590594从数据得知，方程不显著，不存在异方差。white检验Dependent Variable: YMethod: Least SquaresDate: 12/15/11 Time: 19:22Sample: 1983 1993Included observations: 11VariableCoefficientStd. Errort-StatisticProb. X16.4197911.8152133.5366610.0095X2-0.1841540.206215-0.8930230.4015X40.3978840.1926352.0654860.0777C-12961.1022094.99-0.5866080.5759R-squared0.920107 Mean dependent var41359.82Adjusted R-squared0.885866 S.D. dependent var2679.343S.E. of regression905.1798 Akaike info criterion16.72943Sum squared resid5735453. Schwarz criterionu16.87412Log likelihood-88.01188 F-statistic26.87223Durbin-Watson stat2.893375 Prob(F-statistic)0.000325得到SSR1=5735453.，df1=9 修正用加权最小二乘法。SSR2= 727.4728 ，df2=9，计算F=0.00013 对于F0.05（9,9）=3.18显然F F0.05（9,9）,接受同方差假设，不存在方差。5.3 序列相关检验，并对序列相关进行修正。序列相关检验（针对时间序列数据），用残差图法和杜宾-沃尔森法、拉格朗日乘数法进行检验。修正用广义差分法、柯-奥迭代法、杜宾两步法进行修正。通过各种检验与修正后，确定最终模型模型。残差图法杜宾-沃尔森法 ：d值Durbin-Watson stat=2.024219k=3，n=25 查DW表得dL=1.21 du=1.55dud4-dL，无自相关拉格朗日乘数法 ：利用Eviews软件可以进行拉格朗日乘数检验。Breusch-Godfrey Serial Correlation LM Test:F-statistic3.766479 Probability0.019849Obs*R-squared19.34910 Probability0.036046Test Equation:Dependent Variable: RESIDMethod: Least SquaresDate: 12/15/11 Time: 20:00Presample missing value lagged residuals set to zero.VariableCoefficientStd. Errort-StatisticProb. X11.1447100.5755161.9890150.0721X2-0.1033320.050098-2.0625950.0636X4-0.0403390.122115-0.3303390.7473C4701.66213775.410.3413080.7393RESID(-1)-0.3962850.272798-1.4526700.1742RESID(-2)-0.4018220.339067-1.1850810.2610RESID(-3)-0.2907560.273680