R/.later/example-statsmodel.R

In kvasilopoulos/abvar: Vector Autoregression Models

mdl.description

# From exuber
series_names <- function(x) {

}
plot_names <- function(x) {

}

get_K
nseries
nlags

varm::varm(y = data.frame, x = NULL, p = 4)

EstMdl


AR-Stationary 2-Dimensional VAR(4) Model

Effective Sample Size: 241
Number of Estimated Parameters: 18
LogLikelihood: 811.361
AIC: -1586.72
BIC: -1524

Value       StandardError    TStatistic      PValue
___________    _____________    __________    __________

Constant(1)      0.0017164      0.0015988         1.0735        0.28303
Constant(2)        0.31626       0.091961          3.439      0.0005838
AR{1}(1,1)         0.30899       0.063356          4.877     1.0772e-06
AR{1}(2,1)         -4.4834         3.6441        -1.2303        0.21857
AR{1}(1,2)      -0.0031796      0.0011306        -2.8122       0.004921
AR{1}(2,2)          1.3433       0.065032         20.656      8.546e-95
AR{2}(1,1)         0.22433       0.069631         3.2217      0.0012741
AR{2}(2,1)          7.1896          4.005         1.7951       0.072631
AR{2}(1,2)       0.0012375      0.0018631         0.6642        0.50656
AR{2}(2,2)        -0.26817        0.10716        -2.5025       0.012331
AR{3}(1,1)         0.35333       0.068287         5.1742     2.2887e-07
AR{3}(2,1)           1.487         3.9277        0.37858          0.705
AR{3}(1,2)       0.0028594      0.0018621         1.5355        0.12465
AR{3}(2,2)        -0.22709         0.1071        -2.1202       0.033986
AR{4}(1,1)       -0.047563       0.069026       -0.68906        0.49079
AR{4}(2,1)          8.6379         3.9702         2.1757       0.029579
AR{4}(1,2)     -0.00096323      0.0011142       -0.86448        0.38733
AR{4}(2,2)        0.076725       0.064088         1.1972        0.23123


Innovations Covariance Matrix:
  0.0000   -0.0002
-0.0002    0.1167


Innovations Correlation Matrix:
  1.0000   -0.0925
-0.0925    1.0000