Ejemplo de un modelo SVAR.

Se utilizarán los datos USCHANGE.

#SI SON DATOS
#CARGAR EL DATASET
macro <- uschange
head(macro)
##         Consumption     Income Production   Savings Unemployment
## 1970 Q1   0.6159862  0.9722610 -2.4527003 4.8103115          0.9
## 1970 Q2   0.4603757  1.1690847 -0.5515251 7.2879923          0.5
## 1970 Q3   0.8767914  1.5532705 -0.3587079 7.2890131          0.5
## 1970 Q4  -0.2742451 -0.2552724 -2.1854549 0.9852296          0.7
## 1971 Q1   1.8973708  1.9871536  1.9097341 3.6577706         -0.1
## 1971 Q2   0.9119929  1.4473342  0.9015358 6.0513418         -0.1
#CRENANDO EL MODELO
sv <- cbind("Consumption", "Income", "Production ")
colnames(sv) <- cbind( "Consumption", "Income", "Production")
lagselect <- VARselect(uschange[,1:3], lag.max=8,type="const")
lagselect
## $selection
## AIC(n)  HQ(n)  SC(n) FPE(n) 
##      3      1      1      3 
## 
## $criteria
##                 1          2          3          4          5          6
## AIC(n) -1.3394387 -1.3977078 -1.4318152 -1.4186845 -1.4218510 -1.3638700
## HQ(n)  -1.2527933 -1.2460785 -1.2152018 -1.1370871 -1.0752696 -0.9523046
## SC(n)  -1.1257592 -1.0237687 -0.8976165 -0.7242262 -0.5671330 -0.3488924
## FPE(n)  0.2619985  0.2471924  0.2389584  0.2422183  0.2416135  0.2562859
##                 7            8
## AIC(n) -1.3640914 -1.336510404
## HQ(n)  -0.8875420 -0.794976991
## SC(n)  -0.1888542 -0.001013558
## FPE(n)  0.2565724  0.264213694
lagselect$selection
## AIC(n)  HQ(n)  SC(n) FPE(n) 
##      3      1      1      3
lagselect$criteria
##                 1          2          3          4          5          6
## AIC(n) -1.3394387 -1.3977078 -1.4318152 -1.4186845 -1.4218510 -1.3638700
## HQ(n)  -1.2527933 -1.2460785 -1.2152018 -1.1370871 -1.0752696 -0.9523046
## SC(n)  -1.1257592 -1.0237687 -0.8976165 -0.7242262 -0.5671330 -0.3488924
## FPE(n)  0.2619985  0.2471924  0.2389584  0.2422183  0.2416135  0.2562859
##                 7            8
## AIC(n) -1.3640914 -1.336510404
## HQ(n)  -0.8875420 -0.794976991
## SC(n)  -0.1888542 -0.001013558
## FPE(n)  0.2565724  0.264213694
#CREACION DEL MODELO
modelo1<-VAR(uschange[,1:3],p=1,type=c("const"))
modelo1
## 
## VAR Estimation Results:
## ======================= 
## 
## Estimated coefficients for equation Consumption: 
## ================================================ 
## Call:
## Consumption = Consumption.l1 + Income.l1 + Production.l1 + const 
## 
## Consumption.l1      Income.l1  Production.l1          const 
##    0.305100647    0.095172416   -0.007064414    0.454650735 
## 
## 
## Estimated coefficients for equation Income: 
## =========================================== 
## Call:
## Income = Consumption.l1 + Income.l1 + Production.l1 + const 
## 
## Consumption.l1      Income.l1  Production.l1          const 
##      0.6012357     -0.2464750     -0.0704009      0.4801525 
## 
## 
## Estimated coefficients for equation Production: 
## =============================================== 
## Call:
## Production = Consumption.l1 + Income.l1 + Production.l1 + const 
## 
## Consumption.l1      Income.l1  Production.l1          const 
##     0.81260527    -0.03900531     0.40147572    -0.25872051
modelo3<-VAR(uschange[,1:3],p=3,type=c("const"))
modelo3
## 
## VAR Estimation Results:
## ======================= 
## 
## Estimated coefficients for equation Consumption: 
## ================================================ 
## Call:
## Consumption = Consumption.l1 + Income.l1 + Production.l1 + Consumption.l2 + Income.l2 + Production.l2 + Consumption.l3 + Income.l3 + Production.l3 + const 
## 
## Consumption.l1      Income.l1  Production.l1 Consumption.l2      Income.l2 
##    0.244723318    0.071547629   -0.067961564    0.279907354   -0.037718795 
##  Production.l2 Consumption.l3      Income.l3  Production.l3          const 
##   -0.047519326    0.290442590   -0.031534846    0.007272457    0.195136520 
## 
## 
## Estimated coefficients for equation Income: 
## =========================================== 
## Call:
## Income = Consumption.l1 + Income.l1 + Production.l1 + Consumption.l2 + Income.l2 + Production.l2 + Consumption.l3 + Income.l3 + Production.l3 + const 
## 
## Consumption.l1      Income.l1  Production.l1 Consumption.l2      Income.l2 
##     0.60502493    -0.29978239    -0.14642320     0.18335970    -0.09607254 
##  Production.l2 Consumption.l3      Income.l3  Production.l3          const 
##    -0.01222773     0.22258399    -0.09187316     0.14745667     0.31007805 
## 
## 
## Estimated coefficients for equation Production: 
## =============================================== 
## Call:
## Production = Consumption.l1 + Income.l1 + Production.l1 + Consumption.l2 + Income.l2 + Production.l2 + Consumption.l3 + Income.l3 + Production.l3 + const 
## 
## Consumption.l1      Income.l1  Production.l1 Consumption.l2      Income.l2 
##     0.77291963    -0.10822633     0.45384854     0.16983825    -0.11707353 
##  Production.l2 Consumption.l3      Income.l3  Production.l3          const 
##    -0.14368802     0.08864545     0.08229012     0.04149671    -0.32112590
aic1<-summary(modelo1)$logLik
aic1
## [1] -656.2377
aic2<-summary(modelo3)$logLik
aic2
## [1] -624.8263
#Si es menor que 0.05 existe causalidad
GrangerConsumption <-causality(modelo1, cause = 'Consumption')
GrangerConsumption
## $Granger
## 
##  Granger causality H0: Consumption do not Granger-cause Income
##  Production
## 
## data:  VAR object modelo1
## F-Test = 19.441, df1 = 2, df2 = 546, p-value = 6.982e-09
## 
## 
## $Instant
## 
##  H0: No instantaneous causality between: Consumption and Income
##  Production
## 
## data:  VAR object modelo1
## Chi-squared = 46.671, df = 2, p-value = 7.336e-11
GrangerIncome <-causality(modelo1, cause = 'Income')
GrangerIncome
## $Granger
## 
##  Granger causality H0: Income do not Granger-cause Consumption
##  Production
## 
## data:  VAR object modelo1
## F-Test = 2.7691, df1 = 2, df2 = 546, p-value = 0.0636
## 
## 
## $Instant
## 
##  H0: No instantaneous causality between: Income and Consumption
##  Production
## 
## data:  VAR object modelo1
## Chi-squared = 24.678, df = 2, p-value = 4.377e-06
GrangerProduction <-causality(modelo1, cause = 'Production')
GrangerProduction
## $Granger
## 
##  Granger causality H0: Production do not Granger-cause Consumption
##  Income
## 
## data:  VAR object modelo1
## F-Test = 1.066, df1 = 2, df2 = 546, p-value = 0.3451
## 
## 
## $Instant
## 
##  H0: No instantaneous causality between: Production and Consumption
##  Income
## 
## data:  VAR object modelo1
## Chi-squared = 38.816, df = 2, p-value = 3.727e-09
amat <- diag(3)
amat[2,1] <- NA
amat[3,1] <- NA
amat[3,2] <- NA
amat
##      [,1] [,2] [,3]
## [1,]    1    0    0
## [2,]   NA    1    0
## [3,]   NA   NA    1
#Buidling the Model
y <-uschange[,1]
pi <-uschange[,2]
r <-uschange[,3]
sv <- cbind(y,pi, r)
colnames(sv) <- cbind( "Consumption", "Income", "Production")
lagselect <- VARselect(sv, lag.max = 8, type = "both")
lagselect$selection
## AIC(n)  HQ(n)  SC(n) FPE(n) 
##      3      1      1      3
lagselect$criteria
##                 1          2          3          4          5          6
## AIC(n) -1.3184819 -1.3708598 -1.4027937 -1.3916342 -1.3945572 -1.3370508
## HQ(n)  -1.2101753 -1.1975691 -1.1645190 -1.0883755 -1.0263145 -0.9038241
## SC(n)  -1.0513826 -0.9435008 -0.8151751 -0.6437560 -0.4864194 -0.2686533
## FPE(n)  0.2675528  0.2539340  0.2460234  0.2489075  0.2483703  0.2633582
##                 7           8
## AIC(n) -1.3370800 -1.31126773
## HQ(n)  -0.8388692 -0.74807298
## SC(n)  -0.1084229  0.07764899
## FPE(n)  0.2637392  0.27115628
Model1 <- VAR(sv, p = 1, season = NULL, exog = NULL, type = "const")
GrangerConsumption <-causality(Model1, cause = 'Consumption')
GrangerConsumption
## $Granger
## 
##  Granger causality H0: Consumption do not Granger-cause Income
##  Production
## 
## data:  VAR object Model1
## F-Test = 19.441, df1 = 2, df2 = 546, p-value = 6.982e-09
## 
## 
## $Instant
## 
##  H0: No instantaneous causality between: Consumption and Income
##  Production
## 
## data:  VAR object Model1
## Chi-squared = 46.671, df = 2, p-value = 7.336e-11
GrangerIncome <-causality(Model1, cause = 'Income')
GrangerIncome
## $Granger
## 
##  Granger causality H0: Income do not Granger-cause Consumption
##  Production
## 
## data:  VAR object Model1
## F-Test = 2.7691, df1 = 2, df2 = 546, p-value = 0.0636
## 
## 
## $Instant
## 
##  H0: No instantaneous causality between: Income and Consumption
##  Production
## 
## data:  VAR object Model1
## Chi-squared = 24.678, df = 2, p-value = 4.377e-06
GrangerProduction <-causality(Model1, cause = 'Production')
GrangerProduction
## $Granger
## 
##  Granger causality H0: Production do not Granger-cause Consumption
##  Income
## 
## data:  VAR object Model1
## F-Test = 1.066, df1 = 2, df2 = 546, p-value = 0.3451
## 
## 
## $Instant
## 
##  H0: No instantaneous causality between: Production and Consumption
##  Income
## 
## data:  VAR object Model1
## Chi-squared = 38.816, df = 2, p-value = 3.727e-09
SVARMod1 <- SVAR(Model1, Amat = amat, Bmat = NULL, hessian = TRUE, estmethod =
                   c("scoring", "direct"))
SVARMod1
## 
## SVAR Estimation Results:
## ======================== 
## 
## 
## Estimated A matrix:
##             Consumption   Income Production
## Consumption      1.0000  0.00000          0
## Income          -0.5526  1.00000          0
## Production      -0.9320 -0.07075          1
#Impulse Response Functions
SVARog <- irf(SVARMod1, impulse = "Income", response = "Consumption", n.ahead = 50)
SVARog
## 
## Impulse response coefficients
## $Income
##         Consumption
##  [1,]  0.000000e+00
##  [2,]  9.467259e-02
##  [3,]  5.027863e-03
##  [4,]  1.233815e-02
##  [5,]  4.630068e-04
##  [6,]  1.214341e-03
##  [7,] -1.411214e-04
##  [8,]  3.555339e-05
##  [9,] -7.229003e-05
## [10,] -2.093060e-05
## [11,] -1.944287e-05
## [12,] -6.774348e-06
## [13,] -3.905290e-06
## [14,] -1.334068e-06
## [15,] -6.161635e-07
## [16,] -1.877282e-07
## [17,] -7.064740e-08
## [18,] -1.498510e-08
## [19,] -2.981239e-09
## [20,]  1.329536e-09
## [21,]  1.205567e-09
## [22,]  8.764141e-10
## [23,]  4.501993e-10
## [24,]  2.267679e-10
## [25,]  9.881326e-11
## [26,]  4.215141e-11
## [27,]  1.609343e-11
## [28,]  5.870486e-12
## [29,]  1.846745e-12
## [30,]  4.945152e-13
## [31,]  6.676640e-14
## [32,] -2.900571e-14
## [33,] -3.629051e-14
## [34,] -2.382112e-14
## [35,] -1.304240e-14
## [36,] -6.320562e-15
## [37,] -2.843318e-15
## [38,] -1.185882e-15
## [39,] -4.630740e-16
## [40,] -1.660569e-16
## [41,] -5.346010e-17
## [42,] -1.407427e-17
## [43,] -2.057557e-18
## [44,]  8.077811e-19
## [45,]  1.008031e-18
## [46,]  6.742162e-19
## [47,]  3.679190e-19
## [48,]  1.794895e-19
## [49,]  8.062028e-20
## [50,]  3.376855e-20
## [51,]  1.317960e-20
## 
## 
## Lower Band, CI= 0.95 
## $Income
##         Consumption
##  [1,] -6.840747e-18
##  [2,] -3.411111e-02
##  [3,] -1.516998e-02
##  [4,] -4.268286e-03
##  [5,] -6.249442e-03
##  [6,] -1.333009e-03
##  [7,] -2.611465e-03
##  [8,] -7.351655e-04
##  [9,] -8.092767e-04
## [10,] -3.496185e-04
## [11,] -2.410274e-04
## [12,] -7.511273e-05
## [13,] -5.172955e-05
## [14,] -1.555940e-05
## [15,] -1.177256e-05
## [16,] -3.489402e-06
## [17,] -3.247483e-06
## [18,] -8.619194e-07
## [19,] -8.862456e-07
## [20,] -2.619447e-07
## [21,] -3.101633e-07
## [22,] -2.364397e-07
## [23,] -1.530700e-07
## [24,] -8.381822e-08
## [25,] -4.051401e-08
## [26,] -1.618376e-08
## [27,] -5.619281e-09
## [28,] -2.868648e-09
## [29,] -1.509085e-09
## [30,] -7.670181e-10
## [31,] -4.254201e-10
## [32,] -2.252381e-10
## [33,] -1.234563e-10
## [34,] -6.060890e-11
## [35,] -3.607377e-11
## [36,] -3.443429e-11
## [37,] -2.637250e-11
## [38,] -1.677484e-11
## [39,] -9.301491e-12
## [40,] -4.517704e-12
## [41,] -1.871875e-12
## [42,] -6.242671e-13
## [43,] -3.004915e-13
## [44,] -1.621388e-13
## [45,] -8.904601e-14
## [46,] -4.809478e-14
## [47,] -2.565103e-14
## [48,] -1.414689e-14
## [49,] -7.785996e-15
## [50,] -4.259504e-15
## [51,] -3.976493e-15
## 
## 
## Upper Band, CI= 0.95 
## $Income
##        Consumption
##  [1,] 9.604297e-18
##  [2,] 1.812512e-01
##  [3,] 2.348567e-02
##  [4,] 3.254632e-02
##  [5,] 5.350448e-03
##  [6,] 5.410004e-03
##  [7,] 1.280775e-03
##  [8,] 1.009398e-03
##  [9,] 4.219857e-04
## [10,] 3.008361e-04
## [11,] 1.180542e-04
## [12,] 1.018707e-04
## [13,] 4.125159e-05
## [14,] 3.408760e-05
## [15,] 1.629364e-05
## [16,] 9.145986e-06
## [17,] 4.999632e-06
## [18,] 3.008666e-06
## [19,] 1.702550e-06
## [20,] 1.003728e-06
## [21,] 6.002623e-07
## [22,] 3.636651e-07
## [23,] 2.200574e-07
## [24,] 1.332703e-07
## [25,] 8.067797e-08
## [26,] 4.885707e-08
## [27,] 2.958371e-08
## [28,] 1.792058e-08
## [29,] 1.086170e-08
## [30,] 6.583963e-09
## [31,] 3.991120e-09
## [32,] 2.419555e-09
## [33,] 1.466900e-09
## [34,] 8.893953e-10
## [35,] 5.392814e-10
## [36,] 3.270124e-10
## [37,] 1.983078e-10
## [38,] 1.202661e-10
## [39,] 7.294128e-11
## [40,] 4.424162e-11
## [41,] 2.683587e-11
## [42,] 1.627899e-11
## [43,] 9.875661e-12
## [44,] 5.991449e-12
## [45,] 3.635167e-12
## [46,] 2.205686e-12
## [47,] 1.338411e-12
## [48,] 8.121985e-13
## [49,] 4.929027e-13
## [50,] 2.991484e-13
## [51,] 1.815677e-13
plot(SVARog)

SVARinf <- irf(SVARMod1, impulse = "Production", response = "Consumption", n.ahead = 20)
SVARinf
## 
## Impulse response coefficients
## $Production
##         Consumption
##  [1,]  0.000000e+00
##  [2,] -7.064414e-03
##  [3,] -1.169177e-02
##  [4,] -6.127446e-03
##  [5,] -3.628504e-03
##  [6,] -1.608674e-03
##  [7,] -7.524655e-04
##  [8,] -2.963185e-04
##  [9,] -1.183755e-04
## [10,] -4.017054e-05
## [11,] -1.303207e-05
## [12,] -3.064855e-06
## [13,] -3.519346e-07
## [14,]  3.045251e-07
## [15,]  2.888689e-07
## [16,]  1.865157e-07
## [17,]  9.792102e-08
## [18,]  4.727063e-08
## [19,]  2.079743e-08
## [20,]  8.631029e-09
## [21,]  3.304266e-09
## 
## 
## Lower Band, CI= 0.95 
## $Production
##         Consumption
##  [1,]  0.000000e+00
##  [2,] -8.145624e-02
##  [3,] -7.057657e-02
##  [4,] -4.161771e-02
##  [5,] -2.402997e-02
##  [6,] -9.632858e-03
##  [7,] -2.874209e-03
##  [8,] -9.639472e-04
##  [9,] -4.406355e-04
## [10,] -1.525803e-04
## [11,] -5.612430e-05
## [12,] -3.052014e-05
## [13,] -2.238609e-05
## [14,] -1.339015e-05
## [15,] -1.748123e-05
## [16,] -7.431131e-06
## [17,] -5.615084e-06
## [18,] -5.184575e-06
## [19,] -2.395285e-06
## [20,] -1.048493e-06
## [21,] -1.656572e-07
## 
## 
## Upper Band, CI= 0.95 
## $Production
##        Consumption
##  [1,] 0.000000e+00
##  [2,] 4.690224e-02
##  [3,] 3.298990e-02
##  [4,] 1.964891e-02
##  [5,] 1.101225e-02
##  [6,] 6.576399e-03
##  [7,] 3.729255e-03
##  [8,] 2.178941e-03
##  [9,] 1.763493e-03
## [10,] 1.167907e-03
## [11,] 6.548186e-04
## [12,] 3.996561e-04
## [13,] 1.893411e-04
## [14,] 8.796252e-05
## [15,] 4.400138e-05
## [16,] 2.523987e-05
## [17,] 1.447128e-05
## [18,] 8.301648e-06
## [19,] 4.761842e-06
## [20,] 2.732300e-06
## [21,] 1.567837e-06
plot(SVARinf)

SVARrrp <- irf(SVARMod1, impulse = "Income", response = "Production", n.ahead = 20)
SVARrrp
## 
## Impulse response coefficients
## $Income
##          Production
##  [1,]  7.075305e-02
##  [2,] -1.059968e-02
##  [3,]  8.248405e-02
##  [4,]  3.253425e-02
##  [5,]  2.434659e-02
##  [6,]  9.640528e-03
##  [7,]  5.038987e-03
##  [8,]  1.861548e-03
##  [9,]  8.049406e-04
## [10,]  2.616292e-04
## [11,]  9.262320e-05
## [12,]  2.146363e-05
## [13,]  3.803572e-06
## [14,] -1.599002e-06
## [15,] -1.635689e-06
## [16,] -1.152759e-06
## [17,] -6.065361e-07
## [18,] -3.018543e-07
## [19,] -1.331421e-07
## [20,] -5.640810e-08
## [21,] -2.173062e-08
## 
## 
## Lower Band, CI= 0.95 
## $Income
##          Production
##  [1,] -1.225086e-01
##  [2,] -2.112845e-01
##  [3,] -2.512496e-02
##  [4,] -2.921846e-02
##  [5,] -1.080075e-02
##  [6,] -6.039618e-03
##  [7,] -4.227418e-03
##  [8,] -3.316904e-03
##  [9,] -2.182890e-03
## [10,] -1.291353e-03
## [11,] -4.726536e-04
## [12,] -1.957921e-04
## [13,] -7.463458e-05
## [14,] -6.927447e-05
## [15,] -3.051701e-05
## [16,] -1.896773e-05
## [17,] -6.796227e-06
## [18,] -5.662277e-06
## [19,] -4.337523e-06
## [20,] -2.415761e-06
## [21,] -1.205699e-06
## 
## 
## Upper Band, CI= 0.95 
## $Income
##         Production
##  [1,] 3.164263e-01
##  [2,] 2.323470e-01
##  [3,] 2.144482e-01
##  [4,] 9.000559e-02
##  [5,] 5.852702e-02
##  [6,] 2.900950e-02
##  [7,] 1.770909e-02
##  [8,] 9.962025e-03
##  [9,] 6.167390e-03
## [10,] 3.665010e-03
## [11,] 2.231246e-03
## [12,] 1.338747e-03
## [13,] 8.132181e-04
## [14,] 4.903733e-04
## [15,] 2.975741e-04
## [16,] 1.800488e-04
## [17,] 1.093152e-04
## [18,] 6.296912e-05
## [19,] 3.499975e-05
## [20,] 1.944229e-05
## [21,] 1.085980e-05
plot(SVARrrp)

SVARrr <- irf(SVARMod1, impulse = "Production", response = "Income", n.ahead = 20)
SVARrr
## 
## Impulse response coefficients
## $Production
##              Income
##  [1,]  0.000000e+00
##  [2,] -7.040090e-02
##  [3,] -1.515957e-02
##  [4,] -1.442965e-02
##  [5,] -3.971326e-03
##  [6,] -2.435043e-03
##  [7,] -6.650750e-04
##  [8,] -3.228060e-04
##  [9,] -7.115193e-05
## [10,] -2.655184e-05
## [11,] -1.579359e-07
## [12,]  1.434370e-06
## [13,]  2.254806e-06
## [14,]  1.198910e-06
## [15,]  7.033205e-07
## [16,]  3.136942e-07
## [17,]  1.460370e-07
## [18,]  5.772040e-08
## [19,]  2.298121e-08
## [20,]  7.821890e-09
## [21,]  2.528966e-09
## 
## 
## Lower Band, CI= 0.95 
## $Production
##              Income
##  [1,]  0.000000e+00
##  [2,] -1.596091e-01
##  [3,] -7.380612e-02
##  [4,] -4.699539e-02
##  [5,] -1.776556e-02
##  [6,] -7.347497e-03
##  [7,] -2.021279e-03
##  [8,] -9.017944e-04
##  [9,] -2.759781e-04
## [10,] -1.961482e-04
## [11,] -5.768283e-05
## [12,] -5.865909e-05
## [13,] -2.001306e-05
## [14,] -2.514756e-05
## [15,] -1.014659e-05
## [16,] -7.750493e-06
## [17,] -2.664421e-06
## [18,] -1.248012e-06
## [19,] -6.557278e-07
## [20,] -2.954362e-07
## [21,] -1.009045e-07
## 
## 
## Upper Band, CI= 0.95 
## $Production
##             Income
##  [1,] 0.000000e+00
##  [2,] 2.161165e-02
##  [3,] 2.993336e-02
##  [4,] 1.017534e-02
##  [5,] 7.874276e-03
##  [6,] 2.794598e-03
##  [7,] 3.750099e-03
##  [8,] 2.327408e-03
##  [9,] 1.549914e-03
## [10,] 9.853261e-04
## [11,] 4.347813e-04
## [12,] 2.430623e-04
## [13,] 1.109928e-04
## [14,] 4.003842e-05
## [15,] 2.167827e-05
## [16,] 1.301255e-05
## [17,] 8.224569e-06
## [18,] 4.933314e-06
## [19,] 3.692525e-06
## [20,] 2.274746e-06
## [21,] 1.141954e-06
plot(SVARrr)

#Forecast Error Variance Decomposition
SVARfevd <- fevd(SVARMod1, n.ahead = 20)
SVARfevd
## $Consumption
##       Consumption      Income   Production
##  [1,]   1.0000000 0.000000000 0.000000e+00
##  [2,]   0.9920388 0.007917102 4.408288e-05
##  [3,]   0.9920280 0.007809874 1.621407e-04
##  [4,]   0.9918753 0.007930233 1.944721e-04
##  [5,]   0.9918647 0.007929463 2.058702e-04
##  [6,]   0.9918612 0.007930707 2.081142e-04
##  [7,]   0.9918607 0.007930717 2.086051e-04
##  [8,]   0.9918606 0.007930712 2.086812e-04
##  [9,]   0.9918606 0.007930714 2.086932e-04
## [10,]   0.9918606 0.007930714 2.086946e-04
## [11,]   0.9918606 0.007930714 2.086948e-04
## [12,]   0.9918606 0.007930714 2.086948e-04
## [13,]   0.9918606 0.007930714 2.086948e-04
## [14,]   0.9918606 0.007930714 2.086948e-04
## [15,]   0.9918606 0.007930714 2.086948e-04
## [16,]   0.9918606 0.007930714 2.086948e-04
## [17,]   0.9918606 0.007930714 2.086948e-04
## [18,]   0.9918606 0.007930714 2.086948e-04
## [19,]   0.9918606 0.007930714 2.086948e-04
## [20,]   0.9918606 0.007930714 2.086948e-04
## 
## $Income
##       Consumption    Income  Production
##  [1,]   0.2339015 0.7660985 0.000000000
##  [2,]   0.3022299 0.6945325 0.003237589
##  [3,]   0.2997938 0.6968523 0.003353869
##  [4,]   0.2997763 0.6967391 0.003484588
##  [5,]   0.2997914 0.6967145 0.003494098
##  [6,]   0.2997946 0.6967075 0.003497822
##  [7,]   0.2998034 0.6966986 0.003498058
##  [8,]   0.2998049 0.6966970 0.003498116
##  [9,]   0.2998054 0.6966965 0.003498116
## [10,]   0.2998055 0.6966964 0.003498116
## [11,]   0.2998055 0.6966964 0.003498116
## [12,]   0.2998055 0.6966964 0.003498116
## [13,]   0.2998055 0.6966964 0.003498116
## [14,]   0.2998055 0.6966964 0.003498116
## [15,]   0.2998055 0.6966964 0.003498116
## [16,]   0.2998055 0.6966964 0.003498116
## [17,]   0.2998055 0.6966964 0.003498116
## [18,]   0.2998055 0.6966964 0.003498116
## [19,]   0.2998055 0.6966964 0.003498116
## [20,]   0.2998055 0.6966964 0.003498116
## 
## $Production
##       Consumption      Income Production
##  [1,]   0.4840790 0.002569833  0.5133512
##  [2,]   0.6671361 0.001460783  0.3314032
##  [3,]   0.7069721 0.002915772  0.2901122
##  [4,]   0.7177697 0.003047390  0.2791829
##  [5,]   0.7201145 0.003157714  0.2767277
##  [6,]   0.7205968 0.003173565  0.2762296
##  [7,]   0.7206778 0.003178472  0.2761437
##  [8,]   0.7206901 0.003179127  0.2761307
##  [9,]   0.7206916 0.003179258  0.2761291
## [10,]   0.7206918 0.003179272  0.2761289
## [11,]   0.7206918 0.003179274  0.2761289
## [12,]   0.7206918 0.003179274  0.2761289
## [13,]   0.7206918 0.003179274  0.2761289
## [14,]   0.7206918 0.003179274  0.2761289
## [15,]   0.7206918 0.003179274  0.2761289
## [16,]   0.7206918 0.003179274  0.2761289
## [17,]   0.7206918 0.003179274  0.2761289
## [18,]   0.7206918 0.003179274  0.2761289
## [19,]   0.7206918 0.003179274  0.2761289
## [20,]   0.7206918 0.003179274  0.2761289
plot(SVARfevd)

autoplot(forecast(Model1))