计量经济学课程设计.doc

第 27 页 共 27 页计量经济学课程设计第1章 引言 我国经济经历了持续30多年的高速增长，增加了城乡居民的人均收入。人们在满足最基本的生活需求的同时，追求高品质生活方式是一种必然趋势。外出旅游是提高生活品质的重要方式，被长期压抑的居民旅游需求将伴随着其可支配收入的持续增长得到迅速释放。 我国旅游业发展的阶段性特征：我国旅游业起步较晚，但发展迅猛，在国民经济中的地位和作用日益加强。新中国成立前，我国经济萧条，民生凋敝，旅游业发展基本停滞，旅游产业基本没有形成。建国后到改革开放前的30年间，我国旅游业主要局限在为外交和民间往来活动服务的入境旅游，国内旅游基本是一张白纸。1978年，我国接待入境旅游人数180万人，仅占世界的0.7%，居世界第41位；入境旅游收入2.6亿美元，仅占全球的0.038%，居世界第47位。1978年党的十一届三中全会确立改革开放政策，旅游业才算真正起步。邓小平非常重视旅游业，指出“旅游事业大有文章可做，要突出地搞，加快地搞。”30多年来，随着我国经济持续快速发展和居民收入水平较快提高，我国旅游人数和旅游收入都以年均两位数以上的增速持续发展，已经成国民经济的重要产业，成为继住房、汽车之后增长最快的居民消费领域。据有关资料，2010年，我国旅游业总收入1.57万亿元，对经济的直接贡献相当于GDP的2.5%，加上带动其他产业，旅游业对经济的直接和间接贡献总计相当于GDP的8.6%。旅游业直接从业人员1350万人，加上带动其他就业，旅游业直接与间接就业总人数达7600余万人，约占全国就业总数的9.6%。有研究表明，旅游对住宿业贡献率超过90%，对民航和铁路客运业贡献率超过80%，对文化娱乐业贡献率超过50%，对餐饮业和商品零售业贡献率超过40%，旅游消费对社会消费的贡献超过10%。目前，我国已经跃居全球第四大入境旅游接待国和亚洲第一大出境旅游客源国。第2章 构建并分析模型2.1 相关数据表1 模型中所使用的相关数据时间国内游客（百万人次）居民消费水平（元）国民总收入（亿元）就业人员（万人）私人汽车拥有量（万辆）2000744372198562.272085625.3320017843987108683.472797770.782002878430111976573280968.9820038704606135718.9737361219.23200411025138160289.7742641481.66200512125771184575.8746471848.07200613946416217246.6749782333.32200716107572268631753212876.22200817128707318736.7755643501.39200919029514345046.4758284574.912010210310919407137.8761055938.712011264113134479576.1764207326.792012295714699532872.1767048838.62013326216190583196.77697710501.682014361117778636727.27725312339.362.2 构建模型对于已有数据，建立回归模型，假设如下：其中 Y 国内游客（百万人次） X1 居民消费水平（元） X2 国民总收入（亿元） X3 就业人员（万人) X4 私人汽车拥有量（万辆） 是常数项 是随即干扰项2.2.1 散点图对表1中的数据做散点图，如下： 图1 相关数据的散点图2.2.2 最小二乘估计Dependent Variable: YMethod: Least SquaresDate: 06/21/16 Time: 14:19Sample: 2000 2014Included observations: 15VariableCoefficientStd. Errort-StatisticProb.X10.4249760.1334903.1835910.0098X2-0.0052970.002429-2.1805260.0542X30.0685590.0316712.1647300.0557X4-0.0499830.062935-0.7942090.4455C-5273.2422309.879-2.2829080.0456R-squared0.998126Mean dependent var1785.467Adjusted R-squared0.997377S.D. dependent var943.6307S.E. of regression48.33183Akaike info criterion10.85526Sum squared resid23359.66Schwarz criterion11.09128Log likelihood-76.41444Hannan-Quinn criter.10.85275F-statistic1331.653Durbin-Watson stat1.826495Prob(F-statistic)0.000000图2 相关数据的最小二乘估计模型的估计结果为： (-2.282908) （3.183591) （-2.180526） （2.164730) （-0.794209）=0.998126 =0.997377 F=1331.653 D.W=1.826495第3章 回归模型的检验与修正3.1 回归模型的统计检验 3.1.1 拟合优度检验由最小二乘估计的结果得到=0.998126 =0.997377 都接近1，拟合优度较好。 3.1.2 方程总体线性的显著性检验（F检验）当显著性水平=0.05时，(4,10)=3.48 (10)=2.2281,所以四个变量中只有X1 是显著的。3.2 多重共线性的检验与修正3.2.1 简单相关系数 表2X1X2X3X4X110.9963030.8504840.988825X20.99630310.8794070.974283X30.8504840.87940710.790268X40.9888250.9742830.7902681变量之间相关系数较高，说明存在多重共线性。3.2.2 多重共线性的修正采用逐步回归法：首先对Y 分别与X1,X2,X3,X4 做回归，结果如下： Dependent Variable: YMethod: Least SquaresDate: 06/21/16 Time: 14:40Sample: 2000 2014Included observations: 15VariableCoefficientStd. Errort-StatisticProb.X10.2001000.00312064.128890.0000C18.5451930.993500.5983570.5599R-squared0.996849Mean dependent var1785.467Adjusted R-squared0.996606S.D. dependent var943.6307S.E. of regression54.97016Akaike info criterion10.97502Sum squared resid39282.34Schwarz criterion11.06943Log likelihood-80.31268Hannan-Quinn criter.10.97402F-statistic4112.515Durbin-Watson stat1.473655Prob(F-statistic)0.000000 图3=18.54519+0.200100 (0.598357) (64.12889)=0.996849 =0.996606 F=4112.515 D.W=1.473655Dependent Variable: YMethod: Least SquaresDate: 06/21/16 Time: 14:41Sample: 2000 2014Included observations: 15VariableCoefficientStd. Errort-StatisticProb.X20.0051250.00013039.472710.0000C214.791845.960154.6734360.0004R-squared0.991726Mean dependent var1785.467Adjusted R-squared0.991089S.D. dependent var943.6307S.E. of regression89.07685Akaike info criterion11.94044Sum squared resid103150.9Schwarz criterion12.03485Log likelihood-87.55331Hannan-Quinn criter.11.93944F-statistic1558.095Durbin-Watson stat0.985650Prob(F-statistic)0.000000 图4=214.7918+0.005125 (4.673436) (39.47271) =0.991726 =0.991089 F=1558.095 D.W=0.985650Dependent Variable: YMethod: Least SquaresDate: 06/21/16 Time: 14:42Sample: 2000 2014Included observations: 15VariableCoefficientStd. Errort-StatisticProb.X30.5545360.0628108.8287410.0000C-39840.174715.757-8.4483100.0000R-squared0.857059Mean dependent var1785.467Adjusted R-squared0.846064S.D. dependent var943.6307S.E. of regression370.2309Akaike info criterion14.78970Sum squared resid1781922.Schwarz criterion14.88410Log likelihood-108.9227Hannan-Quinn criter.14.78869F-statistic77.94667Durbin-Watson stat0.260036Prob(F-statistic)0.000001 图5=-39840.17+0.554536 (-8.448310) (8.828741)=0.857059 =0.846064 F=77.94667 D.W=0.260036Dependent Variable: YMethod: Least SquaresDate: 06/21/16 Time: 14:43Sample: 2000 2014Included observations: 15VariableCoefficientStd. Errort-StatisticProb.X40.2463120.00811230.362120.0000C715.733946.1553315.507070.0000R-squared0.986094Mean dependent var1785.467Adjusted R-squared0.985024S.D. dependent var943.6307S.E. of regression115.4764Akaike info criterion12.45958Sum squared resid173352.3Schwarz criterion12.55398Log likelihood-91.44681Hannan-Quinn criter.12.45857F-statistic921.8582Durbin-Watson stat0.783083Prob(F-statistic)0.000000 图6=715.7339+0.246312 (15.50707) (30.36212)=0.986094 =0.985024 F=921.8582 D.W=0.783083其中对Y的拟合优度最高，保留作为基变量。将Y关于，做回归，结果如下： Dependent Variable: YMethod: Least SquaresDate: 06/21/16 Time: 15:09Sample: 2000 2014Included observations: 15VariableCoefficientStd. Errort-StatisticProb.X10.2391440.0522044.5809310.0006X2-0.0010050.001341-0.7493020.4681C-18.3870958.51097-0.3142500.7587R-squared0.996990Mean dependent var1785.467Adjusted R-squared0.996488S.D. dependent var943.6307S.E. of regression55.92149Akaike info criterion11.06263Sum squared resid37526.55Schwarz criterion11.20424Log likelihood-79.96973Hannan-Quinn criter.11.06112F-statistic1987.172Durbin-Watson stat1.418274Prob(F-statistic)0.000000 图7=-18.38709+0.239144-0.001005 (-0.314250) (4.580931) (-0.749302)= 0.996990 =0.996488 F=1987.172 D.W=1.418274将Y关于，做回归，结果如下： Dependent Variable: YMethod: Least SquaresDate: 06/21/16 Time: 15:22Sample: 2000 2014Included observations: 15VariableCoefficientStd. Errort-StatisticProb.X10.1939040.00817223.727670.0000X30.0200800.0244240.8221210.4270C-1434.0141767.122-0.8114970.4329R-squared0.997017Mean dependent var1785.467Adjusted R-squared0.996520S.D. dependent var943.6307S.E. of regression55.66850Akaike info criterion11.05356Sum squared resid37187.79Schwarz criterion11.19517Log likelihood-79.90172Hannan-Quinn criter.11.05205F-statistic2005.329Durbin-Watson stat1.556820Prob(F-statistic)0.000000 图8=-1434.014+0.193904+0.020080 (-0.811497) (23.72767) (0.822121)= 0.997017 =0.996520 F=2005.329 D.W=1.556820将Y关于，做回归，结果如下： Dependent Variable: YMethod: Least SquaresDate: 06/21/16 Time: 15:23Sample: 2000 2014Included observations: 15VariableCoefficientStd. Errort-StatisticProb.X10.1966450.0307066.4040850.0000X40.0043000.0380030.1131390.9118C30.37670109.43230.2775850.7861R-squared0.996852Mean dependent var1785.467Adjusted R-squared0.996328S.D. dependent var943.6307S.E. of regression57.18426Akaike info criterion11.10729Sum squared resid39240.48Schwarz criterion11.24890Log likelihood-80.30468Hannan-Quinn criter.11.10578F-statistic1900.115Durbin-Watson stat1.476242Prob(F-statistic)0.000000 图9=30.37670+0.196645+0.004300 (0.277585) (6.404085) (0.113139)=0.996852 =0.996328 F=1900.115 D.W=1.476242由回归结果可知,的显著性都小于(10)=2.2281，且的系数不符合其经济意义，因此只保留作为Y的基变量。 最终的模型为：=18.54519+0.2001003.3 异方差的检验与修正3.2.1怀特检验 Heteroskedasticity Test: White F-statistic1.496206Prob. F(2,12)0.2629Obs*R-squared2.993926Prob. Chi-Square(2)0.2238Scaled explained SS1.614510Prob. Chi-Square(2)0.4461Test Equation:Dependent Variable: RESID2Method: Least SquaresDate: 06/21/16 Time: 16:18Sample: 2000 2014Included observations: 15VariableCoefficientStd. Errort-StatisticProb.C-2638.8304397.836-0.6000290.5596X11.4543981.0151451.4327000.1775X12-7.69E-054.86E-05-1.5806090.1400R-squared0.199595Mean dependent var2618.822Adjusted R-squared0.066194S.D. dependent var3248.257S.E. of regression3138.909Akaike info criterion19.11799Sum squared resid1.18E+08Schwarz criterion19.25960Log likelihood-140.3850Hannan-Quinn criter.19.11649F-statistic1.496206Durbin-Watson stat2.502178Prob(F-statistic)0.262941 图10在=0.05的显著性水平下，（2）=5.99，=2.9939255.99,故由怀特检验得异方差性不存在。3.2.2 G-Q检验（1） 前6组数据如下： 表3国内游客（百万人次）居民消费水平（元）74437217843987878430187046061102513812125771做最小二乘估计： Dependent Variable: Y1Method: Least SquaresDate: 06/21/16 Time: 18:09Sample: 1 6Included observations: 6VariableCoefficientStd. Errort-StatisticProb.X10.2407970.0421375.7146260.0106C-172.0129184.4956-0.9323420.4199R-squared0.915865Mean dependent var875.6000Adjusted R-squared0.887820S.D. dependent var138.7473S.E. of regression46.47103Akaike info criterion10.80471Sum squared resid6478.669Schwarz criterion10.64848Log likelihood-25.01177Hannan-Quinn criter.10.38542F-statistic32.65696Durbin-Watson stat2.831448Prob(F-statistic)0.010631 图11（2）后6组数据如下： 表4国内游客（百万人次）居民消费水平（元）19029514210310919264113134295714699326216190361117778做最小二乘估计： Dependent Variable: Y2Method: Least SquaresDate: 06/21/16 Time: 18:13Sample: 2010 2014Included observations: 6VariableCoefficientStd. Errort-StatisticProb.X20.2174500.00468846.386880.0000C-247.799669.08554-3.5868520.0371R-squared0.998608Mean dependent var2914.800Adjusted R-squared0.998144S.D. dependent var579.0097S.E. of regression24.94699Akaike info criterion9.560557Sum squared resid1867.056Schwarz criterion9.404333Log likelihood-21.90139Hannan-Quinn criter.9.141265F-statistic2151.743Durbin-Watson stat2.222186Prob(F-statistic)0.000022 图12=4.28,F=6478.669/1867.056=3.46994.28,因此由G-Q检验得到异方差性不存在。3.4 序列相关性的检验与修正3.3.1 D.W 检验法由表5可以得到D.W=1.473655，=1.36,=1.08,1.4736554-，因此得到一阶序列相关性不存在。3.3.2 拉格朗日检验一阶序列相关性的检验如下：Breusch-Godfrey Serial Correlation LM Test:F-statistic0.791936Prob. F(1,12)0.3910Obs*R-squared0.928636Prob. Chi-Square(1)0.3352Test Equation:Dependent Variable: RESIDMethod: Least SquaresDate: 06/27/16 Time: 16:10Sample: 2000 2014Included observations: 15Presample missing value lagged residuals set to zero.VariableCoefficientStd. Errort-StatisticProb.X0.0003380.0031680.1067200.9168C-2.38961731.35971-0.0762000.9405RESID(-1)0.2549190.2864560.8899080.3910R-squared0.061909Mean dependent var6.25E-14Adjusted R-squared-0.094439S.D. dependent var52.97057S.E. of regression55.41540Akaike info criterion11.04445Sum squared resid36850.40Schwarz criterion11.18606Log likelihood-79.83337Hannan-Quinn criter.11.04294F-statistic0.395968Durbin-Watson stat2.027080Prob(F-statistic)0.681506 图13（1）=3.84，=0.9286363.84,得到一阶序列相关性不存在。3.5 滞后变量的讨论采用阿尔蒙多项式法。滞后期的检验结果如下： 图14由结果可以看出，滞后期为5.做滞后5期的最小二乘估计： 图15回