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ID:15779728
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时间:2018-08-05
《(删除变量u)adf检验及模型函数估计》由会员上传分享,免费在线阅读,更多相关内容在教育资源-天天文库。
1、删除变量M2/M0),建立模型:经检验:所有序列(变量)二阶(差分)单整,故可以得出他们之间存在协整关系。一、对所构造的函数形式:lslnm1clngdplnvfruu进行OLS估计得:DependentVariable:LNM1Method:LeastSquaresDate:07/17/11Time:16:07Sample:19932009Includedobservations:17VariableCoefficientStd.Errort-StatisticProb. C-0.7110690.122437-5.8076510.0001LNGDP0
2、.8897460.03479725.569320.0000LNV0.0252900.0120652.0962220.0600F-0.0045460.000838-5.4245750.0002R0.0116610.0037103.1428340.0094UU0.8197890.07002911.706480.0000R-squared0.999608 Meandependentvar6.379730AdjustedR-squared0.999429 S.D.dependentvar0.800568S.E.ofregression0.019128
3、 Akaikeinfocriterion-4.804743Sumsquaredresid0.004025 Schwarzcriterion-4.510668Loglikelihood46.84032 Hannan-Quinncriter.-4.775512F-statistic5603.099 Durbin-Watsonstat2.559494Prob(F-statistic)0.000000因为变量LnV不显著,将其删除,重新估计:lslnm1clngdpfruuDependentVariable:LNM1Method:Leas
4、tSquaresDate:07/17/11Time:16:10Sample:19932009Includedobservations:17VariableCoefficientStd.Errort-StatisticProb. C-0.8718970.108072-8.0677730.0000LNGDP0.9497760.02238942.421070.0000F-0.0051480.000892-5.7726120.0001R0.0085100.0038422.2149540.0469UU0.7678080.07417710.351000.0000R
5、-squared0.999451 Meandependentvar6.379730AdjustedR-squared0.999268 S.D.dependentvar0.800568S.E.ofregression0.021665 Akaikeinfocriterion-4.586298Sumsquaredresid0.005633 Schwarzcriterion-4.341236Loglikelihood43.98354 Hannan-Quinncriter.-4.561939F-statistic5458.784
6、 Durbin-Watsonstat2.600129Prob(F-statistic)0.000000所以:LNM1=-0.871897137169+0.949776234674*LNGDP-0.00514818980363*F+0.00851005559974*R+0.767808226881*UU……(1);且各个变量均通过显著性水平0.05的检验。二、对回归的残差序列E1进行平稳性检验:NullHypothesis:E1hasaunitrootExogenous:ConstantLagLength:0(AutomaticbasedonSIC,MAX
7、LAG=3)t-Statistic Prob.*AugmentedDickey-Fullerteststatistic-5.107454 0.0011Testcriticalvalues:1%level-3.9203505%level-3.06558510%level-2.673459其中E1为当前模型的残差序列,由输出结果,可见序列E无单位根,LnM1与各序列协整。短期动态货币需求函数估计:按照Hendry建模理论(年度数据从滞后2期开始,季度数据从滞后8期开始删除不显著变量,本实例中我们均采用年度数据,故从滞后2期开始),首先建立一个能够代表数据生
8、成过程(DGP)的自回归分布滞后模型(ADL),然后逐步简化,最后得到包含变量间
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