summary of a conjugate Normal-Inverse-Wishart bayesian VAR model

summary of a conjugate Normal-Inverse-Wishart bayesian VAR model

summary_conjugate(model)

Arguments

model	estimated conjugate N-IW model

Value

nothing

Details

summary of a conjugate Normal-Inverse-Wishart bayesian VAR model

Examples

data(Yraw)
priors <- Carriero_priors(Yraw, p = 4)
model <- bvar_conjugate0(priors = priors, keep = 100)
summary_conjugate(model)
#> Number of lags, p = 4
#> Number of endogeneos variables, m = 3
#> Number of exogeneos variables (including constant), d = 1
#> Number of parameters, k = mp + d = 13
#> Initial number of observations, T_in = 215
#> Number of dummy observations, T_dummy = 4
#> Number of observations available for regression, T = T_in + T_dummy - p = 215
#> Posterior mean of Phi (VAR coefficients) [k = 13 x m = 3]:
#>                    eq_inflation eq_unemployment eq_interest_rate
#> inflation, l=1      1.324303477     0.026892371      0.326364798
#> unemployment, l=1  -0.192550805     1.171699260     -0.429377021
#> interest_rate, l=1 -0.026508511    -0.029864388      0.832926444
#> inflation, l=2     -0.221426553    -0.011118977     -0.178665196
#> unemployment, l=2   0.065438617    -0.199306705      0.303542308
#> interest_rate, l=2  0.019102471     0.016428946     -0.003814684
#> inflation, l=3     -0.085212104    -0.015393879      0.051025071
#> unemployment, l=3   0.041503852    -0.087072758      0.053237955
#> interest_rate, l=3  0.004068285     0.036506618      0.060082575
#> inflation, l=4     -0.020719579     0.030347152     -0.098299318
#> unemployment, l=4   0.038393564     0.003018880      0.092319262
#> interest_rate, l=4 -0.001007131     0.005102153      0.012306069
#> const               0.313102595     0.396227579      0.055204953
#> Posterior nu = 220
#> Number of mcmc simulations, keep = 100
#> Posterior sample mean of Phi (VAR coefficients) [k = 13 x m = 3]:
#>                    eq_inflation eq_unemployment eq_interest_rate
#> inflation, l=1      1.330246962    0.0284986404      0.349542040
#> unemployment, l=1  -0.191926521    1.1680977979     -0.427221568
#> interest_rate, l=1 -0.030854418   -0.0345532734      0.837195028
#> inflation, l=2     -0.223846714   -0.0078585248     -0.200288463
#> unemployment, l=2   0.060916506   -0.1960037209      0.297352327
#> interest_rate, l=2  0.023887164    0.0213538978     -0.011797213
#> inflation, l=3     -0.086420004   -0.0205153024      0.044595195
#> unemployment, l=3   0.046238059   -0.0837527801      0.063238961
#> interest_rate, l=3  0.001742788    0.0382274451      0.065206082
#> inflation, l=4     -0.020837723    0.0292497574     -0.092349386
#> unemployment, l=4   0.039361325   -0.0004856565      0.085854481
#> interest_rate, l=4 -0.001770026    0.0032800096      0.009587561
#> const               0.314742095    0.4039635791      0.061434197
#> Posterior sample mean of Sigma (noise covariance matrix) [m = 3 x m = 3]:
#>                  inflation unemployment interest_rate
#> inflation      0.098315459 -0.005703899     0.0361328
#> unemployment  -0.005703899  0.109746446    -0.1004776
#> interest_rate  0.036132799 -0.100477559     0.5811884
#> Posterior sample sd of Phi (VAR coefficients) [k = 13 x m = 3]:
#>                    eq_inflation eq_unemployment eq_interest_rate
#> inflation, l=1       0.04798957      0.05018234       0.12031091
#> unemployment, l=1    0.04897122      0.05955965       0.12882452
#> interest_rate, l=1   0.02724645      0.02765532       0.06870363
#> inflation, l=2       0.06581088      0.06790946       0.16418661
#> unemployment, l=2    0.06014113      0.07209913       0.14713618
#> interest_rate, l=2   0.02390045      0.02730760       0.06633464
#> inflation, l=3       0.04926938      0.03999189       0.12283949
#> unemployment, l=3    0.04471902      0.05291467       0.11250371
#> interest_rate, l=3   0.02031160      0.02001106       0.04607689
#> inflation, l=4       0.03377313      0.03133525       0.08381856
#> unemployment, l=4    0.03275600      0.03917304       0.07667082
#> interest_rate, l=4   0.01533477      0.01561504       0.03581872
#> const                0.11707786      0.10664041       0.26004503
#> Posterior sample sd of Sigma (noise covariance matrix) [m = 3 x m = 3]:
#>                 inflation unemployment interest_rate
#> inflation     0.009551238  0.006501589    0.01386120
#> unemployment  0.006501589  0.011090799    0.02073147
#> interest_rate 0.013861202  0.020731465    0.06091633