原始数据四阶格兰杰因果检验

1、.tsset Ttime variable: T, 1 to 132delta: 1 unit.var IM RMB,lagsoption lags not allowedr(198);.var IM RMBVector autoregressionSample: 3 - 132 No. of obs = 130Log likelihood = -3709.412 AIC = 57.22173FPE = 2.43e+22 HQIC = 57.31136Det(Sigma ml) = 2.09e+22 SBIC = 57.44231Equation Parms RMSE R-sq chi2 Pc

2、hi2IM 5 1.3e+07 0.9155 1408.737 0.0000RMB 5 11791.5 0.9988 108504.9 0.0000Coef. Std. Err. z Pz 95% Conf. IntervalIMIML1. .5015445 .083567 6.00 0.000 .3377562 .6653329L2. .3035912 .0848754 3.58 0.000 .1372384 .4699439RMBL1. 127.3601 72.35141 1.76 0.078 -14.44604 269.1663L2. -106.7493 71.87691 -1.49 0

3、.137 -247.6254 34.12691 cons 1.52e+07 4788773 3.18 0.001 5840482 2.46e+07RMBIML1. .0001371 .0000774 1.77 0.076 -.0000145 .0002887L2. 9.68e-06 .0000786 0.12 0.902 -.0001443 .0001637RMBL1. 1.658331 .0669717 24.76 0.000 1.527069 1.789593L2. -.6740827 .0665325 -10.13 0.000 -.804484 -.5436814cons -8701.3

4、49 4432.704 -1.96 0.050 -17389.29 -13.40801 .vargrangerGranger causality Wald tests+Equation Excluded chi2 df Prob chi21+IM RMB 8.6221 2 0.013IM ALL 8.6221 2 0.0131+RMB IM 7.0178 2 0.030RMB ALL 7.0178 2 0.030+.var EX RMBVector autoregressionSample: 3 - 132 No. of obs = 130Log likelihood = -3739.517

5、AIC = 57.68487FPE = 3.87e+22 HQIC = 57.7745Det(Sigma_ml) = 3.31e+22 SBIC = 57.90545Equation Parms RMSE R-sq chi2 Pchi2EX 5 1.6e+07 0.8984 1149.73 0.0000RMB 5 11869.9 0.9988 107075 0.0000Coef. Std. Err. z Pz 95% Conf. IntervalEXEXL1. .6025429 .0862089 6.99 0.000 .4335765 .7715092L2. .1712488 .0859085

6、 1.99 0.046 .0028712 .3396265RMBL1. 100.3899 89.1941 1.13 0.260 -74.42728 275.2072L2. -71.26326 89.35772 -0.80 0.425 -246.4012 103.8746 cons 2.07e+07 5572072 3.71 0.000 9740162 3.16e+07RMBEXL1. .0000271 .0000641 0.42 0.673 -.0000986 .0001528L2. .0000764 .0000639 1.19 0.232 -.0000489 .0002016RMBL1. 1

7、.673043 .0663563 25.21 0.000 1.542987 1.8031L2. -.685192 .0664781 -10.31 0.000 -.8154866 -.5548974_cons -6914.907 4145.367 -1.67 0.095-15039.68 1209.863.vargrangerGranger causality Wald tests+Equation Excluded chi2 df Prob chi21+EX RMB 11.497 2 0.003EX ALL 11.497 2 0.0031+RMB EX 5.2143 2 0.074RMB AL

8、L 5.2143 2 0.074+tsset Ttime variable: T, 1 to 132 delta: 1 unit.var IM RMB,lagsoption lags not allowed r(198);.var IM RMBVector autoregressionSample: 3 - 132Log likelihood = -3709.412FPE= 2.43e+22Det(Sigma_ml) = 2.09e+22No. of obs=AIC=HQICSBIC13057.22173=57.31136= 57.44231EquationParmsRMSER-sqchi2P

9、chi2IMRMB551.3e+0711791.50.91550.99881408.737108504.90.00000.0000|-LCoef.Std. Err.zP|z|95% Conf. Interval+-IM|IM |L1. |.5015445.0835676.000.000.3377562.6653329L2. |1.3035912.08487543.580.000.1372384.4699439|RMB |L1. |127.360172.351411.760.078-14.44604269.1663L2. |1-106.749371.87691-1.490.137-247.625

10、434.12691|_cons |-L1.52e+0747887733.180.00158404822.46e+07+-RMB|IM |L1. |.0001371.00007741.770.076-.0000145.0002887L2. |19.68e-06.00007860.120.902-.0001443.0001637|RMB |L1. |1.658331.066971724.760.0001.5270691.789593L2. |1-.6740827.0665325-10.130.000-.804484-.5436814|_cons |-8701.3494432.704-1.960.0

11、50-17389.29-13.40801.vargrangerGranger causality Wald tests+|EquationExcluded | chi2 df Prob chi2 |IMIM+-LRMB |ALL |-|8.62218.6221220.0130.013|+RMBRMBIM |ALL |-|7.01787.0178-+220.0300.030.var EX RMBVector autoregressionSample:3 - 132No. of obs=130Log likelihood = -3739.517AIC=57.68487FPE=3.87e+22HQI

12、C=57.7745Det(Sigma_ml):3.31e+22SBIC=57.90545EquationParmsRMSER-sqchi2Pchi2EX51.6e+070.89841149.730.0000RMB511869.90.99881070750.0000|Coef.Std. Err.zP|z|95% Conf. Interval-+EX|EX |L1. |.6025429.08620896.990.000.4335765.7715092L2. |1.1712488.08590851.990.046.0028712.3396265|RMB |L1. |100.389989.19411.

13、130.260-74.42728275.2072L2. |1-71.2632689.35772-0.800.425-246.4012103.8746| cons |2.07e+0755720723.710.00097401623.16e+07-+RMB|EX |L1. |.0000271.00006410.420.673-.0000986.0001528L2. |1.0000764.00006391.190.232-.0000489.0002016|RMB |L1. |1.673043.066356325.210.0001.5429871.8031L2. |1-.685192.0664781-10.310.000-.8154866-.5548974| cons |-6914.9074145.367-1.67