.tsset Ttime variable: T, 1 to 132delta: 1 unit.var IM RMB,lagsoption lags not allowed r(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 Rsq chi2 Pchi2IM 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. Int ervalIMIML1. .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 0.137 247.6254 34.12691 cons 1.52e+07 4788773 3.18 0.001 5840482 2.46e+07RMBIML1. .0001371 .0000774 1.77 0.076 .0000145 .0002887L2. 9.68e06 .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 8701.349 4432.704 1.96 0.050 17389.29 13.40801 .vargrangerGranger causality Wald tests+Equation Excluded chi2 df Prob chi211IM RMB 8.6221 2 0.013IM ALL 8.6221 2 0.01311RMB 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 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. Int ervalEXEXL1. .6025429 .0862089 6.99 0.000 .4335765 .7715092L2. .1712488 .0859085 1.99 0.046 .0028712 .3396265RMBLI. 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 .0002016RMBLI. 1.673043 .0663563 25.21 0.000 1.542987 1.8031L2. -.685192 .0664781 -10.31 0.000 -.8154866 -.5548974cons -6914.907 4145.367 -1.67 0.095 -15039.68 1209.863.vargrangerGranger causality Wald tests+Equation Excluded chi2 df Prob chi211EX RMB 11.497 2 0.003EX ALL 11.497 2 0.00311RMB EX 5.2143 2 0.074RMB ALL 5.2143 2 0.074+tsset Ttime variable: T, 1 to 132 delta: 1 unit. var IM RMB,lags option lags not allowed r(198);. var IM RMBVector autoregressionSample: 3 - 132Log likelihood = -3709.412FPE = 2.43e+22Det(Sigma_ml) = 2.09e+22No. of obs= 130AIC = 57.22173HQIC = 57.31136SBIC = 57.44231EquationParmsRMSER-sqchi2Pchi2IM51.3e+070.91551408.7370.0000RMB511791.50.9988108504.90.000095% Conf. Interval| Coef. Std. Err. z P|z|+IM |IM |L1. |.5015445.0835676.000.000.3377562.6653329L2. |.3035912.08487543.580.000.1372384.4699439| RMB |L1. |127.360172.351411.760.078-14.44604269.1663L2. |1-106.749371.87691-1.490.137-247.625434.12691|_cons |1.52e+0747887733.180.00158404822.46e+07+RMB |IM |L1. |.0001371.00007741.770.076-.0000145.0002887L2. |9.68e-06.00007860.120.902-.0001443.0001637| RMB |L1. |1.658331.066971724.760.0001.5270691.789593L2. |-.6740827.0665325-10.130.000-.804484-.5436814| cons |-8701.3494432.704-1.960.050-17389.29-13.40801. vargrangerGranger causality Wald tests+-|IMRMB |8.622120.013IMALL |8.622120.013-L+-|-|RMBIM |7.017820.030RMBALL |7.017820.030| EquationExcluded | chi2df Prob chi2 |+. var EX RMBVector autoregressionSample: 3 - 132Log likelihood = -3739.517FPE = 3.87e+22Det(Sigma_ml) = 3.31e+22No. of obs= 130AIC = 57.68487HQIC = 57.7745SBIC = 57.90545EquationParmsRMSER-sqchi2Pchi2EX51.6e+070.89841149.730.0000RMB511869.90.99881070750.0000|-LCoef.Std. Err.zP|z|95% Conf. IntervalEX| EX |L1. |.6025429.08620896.990.000.4335765.7715092L2. |.1712488.08590851.990.046.0028712.3396265|RMB |L1. |100.389989.19411.130.260-74.42728275.2072L2. |I-71.2632689.35772-0.800.425-246.4012103.8746|_cons |-L+2.07e+0755720723.710.00097401623.16e+07RMB|EX |L1. |.0000271.00006410.420.673-.0000986.0001528L2. |.0000764.00006391.190.232-.0000489.0002016|RMB |L1. |1.673043.066356325.210.0001.5429871.8031L2. |I-.685192.0664781-10.310.000-.8154866-.5548974| ons |-6914.9074145.367-1.670.095-15039.681209.863. vargrangerGranger causality Wald tests+|Equation+-L+-Excluded |chi2-|df Prob chi2 |EX