单方程第6题误差修正模型ECM例5.10财政支出和财政收入的协整关系检验

1、财政收入和财政支出的关系图：新建workfile，注意开始日期和截止日期：把数据载入workfile中，取名：f_ex和f_in.把两个变量作为一个组打开，点击view/garph画图，直接点击ok。单位根检验表面：财政支出和财政收入都是一阶单整，可以探讨两者的协整关系。具体操作步骤：先单独打开f_ex,点击view/unit root test.然后点击ok。如下图所示。接下来，要再次重复这个动作，只是选项中药选择1 st difference。如下图所示点击ok。单位根检验如下表：Null Hypothesis: F_EX has a unit rootExogenous: Constan

2、tLag Length: 11 (Automatic - based on SIC, maxlag=14)t-StatisticProb.*Augmented Dickey-Fuller test statistic17.491491.0000Test critical values:1% level-3.4624125% level-2.87553810% level-2.574309Null Hypothesis: D(F_EX) has a unit rootExogenous: Constant, Linear TrendLag Length: 13 (Automatic - base

3、d on SIC, maxlag=14)t-StatisticProb.*Augmented Dickey-Fuller test statistic-4.5954680.0014Test critical values:1% level-4.0043655% level-3.43233910% level-3.139924Null Hypothesis: F_IN has a unit rootExogenous: Constant, Linear TrendLag Length: 11 (Automatic - based on SIC, maxlag=14)t-StatisticProb

4、.*Augmented Dickey-Fuller test statistic8.4344061.0000Test critical values:1% level-4.0036755% level-3.43200510% level-3.139728Null Hypothesis: D(F_IN) has a unit rootExogenous: Constant, Linear TrendLag Length: 12 (Automatic - based on SIC, maxlag=14)t-StatisticProb.*Augmented Dickey-Fuller test st

5、atistic-4.6826840.0010Test critical values:1% level-4.0041325% level-3.43222610% level-3.139858把f_ex和f_in以一个group打开，点击view/contegration test/Johansen system.然后点击ok。两者之间的协整关系检验如下表：Data Trend:NoneNoneLinearLinearQuadraticTest TypeNo InterceptInterceptInterceptInterceptInterceptNo TrendNo TrendNo Trend

6、TrendTrendTrace22211Max-Eig22211再次点击view/granger causality两者之间格兰杰因果关系检验如下表：Pairwise Granger Causality TestsDate: 01/04/14 Time: 19:37Sample: 1990M01 2007M12Lags: 2 Null Hypothesis:ObsF-StatisticProb.  F_EX does not Granger Cause F_IN 214 51.54936.E-19 F_IN does not Gran

7、ger Cause F_EX 63.32413.E-22接下来要对f_ex和f_in进行差分。把这两个变量作为一个组打开，点击Default选项下的log，把数据复制到excel中，重新命名ln(f_ex)和ln(f_in),按照上面说的步骤把数据重新载入eviews，或者之前就在excel中对数据进行取对数，需要注意的是，上面的画图、协整关系、格兰杰因果关系检验也可以对ln(f_ex)和ln(f_in)进行。建立方程：点击proc/make equation.得到下面的结果。Dependent Variable: LNF_EXMethod: Least SquaresDate: 0

8、1/04/14 Time: 20:02Sample: 1 217Included observations: 217VariableCoefficientStd. Errort-StatisticProb.  LNF_IN0.9565130.02489038.430030.0000C0.3347910.1702761.9661700.0506R-squared0.872921    Mean dependent var6.818918Adjusted R-squared0.872330    S

9、.D. dependent var0.945148S.E. of regression0.337710    Akaike info criterion0.675914Sum squared resid24.52031    Schwarz criterion0.707066Log likelihood-71.33672    Hannan-Quinn criter.0.688498F-statistic1476.867    Durb

10、in-Watson stat1.754325Prob(F-statistic)0.000000估计结果：第二步，令resid=，即将上式的残差序列作为误差修正项，点击鼠标右键复制resid，然后粘贴命名为ecm，建立下面的误差修正模型：估计结果如下表：复制lnf_ex、lnf_in，粘贴他们，重新命名ln(f_ex)_01、ln(f_in)_01，并对它们取differenced。ln(f_ex)_01、ln(f_in)_01和ecm,作为一个组打开还是老样子点击proc/make equation.，为防止过于繁琐，步骤省略如下：估计方程。如下图所示。Dependent Variable:

11、LNF_EXMethod: Least SquaresDate: 01/04/14 Time: 20:30Sample (adjusted): 2 217Included observations: 216 after adjustmentsVariableCoefficientStd. Errort-StatisticProb.  ECM(-1)0.0902650.0712461.2669510.2066LNF_IN0.9536890.02517337.885670.0000C0.3550970.1724242.0594440.0407R-squared0.871564&

12、#160;   Mean dependent var6.827656Adjusted R-squared0.870358    S.D. dependent var0.938518S.E. of regression0.337921    Akaike info criterion0.681782Sum squared resid24.32259    Schwarz criterion0.728661Log likelihood-70.6324

13、7    Hannan-Quinn criter.0.700721F-statistic722.7079    Durbin-Watson stat1.908519Prob(F-statistic)0.000000一、 残差检验：1、正态性检验如下图：view/residual diagnostic /histogram.2.序列相关LM检验如下表：view/residual diagnostic /serial correlationBreusch-Godfrey Serial Correlation LM Te

14、st:F-statistic45963.72    Prob. F(2,211)0.0000Obs*R-squared215.5045    Prob. Chi-Square(2)0.00003.white异方差检验如下表：view/residual diagnostic /Heteroskedasticity test/Heteroskedasticity Test: WhiteF-statistic3326.580    Prob. F(5,210)0.0000Obs*R

15、-squared213.3069    Prob. Chi-Square(5)0.0000Scaled explained SS96.99202    Prob. Chi-Square(5)0.0000二、模型稳定性检验1、 Chow分割点检验如下表：view/stability diagnostics /chow breakpoint test Chow Breakpoint Test: 100 Null Hypothesis: No breaks at specified breakpointsVar

16、ying regressors: All equation variablesEquation Sample: 1 216F-statistic30219.64Prob. F(3,210)0.0000Wald Statistic 90658.92Prob. Chi-Square(3)0.00002、Chou预测检验如下表：view/stability diagnostics /chow forecast.Chow Forecast TestEquation: UNTITLEDSpecification: LNF_EX ECM LNF_IN CTest predictions for

17、observations from 200 to 216ValuedfProbabilityF-statistic-10.25718(17, 196) 1.0000F-test summary:Sum of Sq.dfMean SquaresTest SSR-4.70E-25 17-2.76E-26Restricted SSR 5.83E-26 213 2.74E-28Unrestricted SSR 5.28E-25 196 2.70E-27Unrestricted SSR 5.28E-25 196 2.70E-27Unrestricted log likelihood adjusts test equation results to account for        observations