Summarise a fitted steady-state BVAR model

Computes and prints posterior summaries from a fitted steady-state bvar object. The printed output depends on whether the model is homoscedastic or includes stochastic volatility (RW or AR1 specification).

Usage

# S3 method for class 'bvar'
summary(object, pars = NULL, stat = "mean", t = NULL, ...)

Arguments

object

A steady-state bvar object that has been passed through fit.

pars

Character vector of parameter names to include. If NULL (default), all available parameters are displayed.

stat

Character. Posterior summary statistic to display: "mean" (default) or "median".

t

Integer. Time index for the innovation covariance matrix if stochastic volatility was estimated. If NULL, the latest available t is used.

...

Additional arguments (currently unused).

Value

Returns the input object invisibly.

Details

The function summarises the following estimated parameters:

• beta: \(kp \times k\) VAR coefficient matrix

• Psi: \(k \times q\) steady-state parameter matrix

• Sigma_u: innovation covariance matrix (\(k \times k\) for homoscedastic, \(T \times k \times k\) for stochastic volatility)

• If Random Walk stochastic volatility:

  • A: \(k \times k\) lower triangular matrix with ones on the diagonal that describes the contemporaneous interaction of the endogenous variables

  • phi: \(k\)-dimensional vector of log volatility innovation variances

• If AR1 stochastic volatility:

  • A: \(k \times k\) lower triangular matrix with ones on the diagonal that describes the contemporaneous interaction of the endogenous variables

  • gamma_0: \(k\)-dimensional vector of log volatility intercepts

  • gamma_1: \(k\)-dimensional vector of log volatility slopes

  • Phi: \(k \times k\) log volatility innovation covariance matrix

Examples

# \donttest{
yt <- matrix(rnorm(20), 10, 2)
bvar_obj <- bvar(data = yt)
bvar_obj <- setup(bvar_obj, p = 1, deterministic = "constant")
bvar_obj <- priors(bvar_obj,
                   theta_Psi = rep(0, 2),
                   Omega_Psi = diag(0.1, 2, 2))

bvar_obj <- fit(bvar_obj,
                H = 1,
                d_pred = matrix(1),
                iter = 100,
                warmup = 50,
                chains = 1,
                cores = 1)
#> 
#> SAMPLING FOR MODEL 'steady_state_bvar_homoscedastic_jeffreys_prior' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 4.8e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.48 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
#> Chain 1: WARNING: There aren't enough warmup iterations to fit the
#> Chain 1:          three stages of adaptation as currently configured.
#> Chain 1:          Reducing each adaptation stage to 15%/75%/10% of
#> Chain 1:          the given number of warmup iterations:
#> Chain 1:            init_buffer = 7
#> Chain 1:            adapt_window = 38
#> Chain 1:            term_buffer = 5
#> Chain 1: 
#> Chain 1: Iteration:  1 / 100 [  1%]  (Warmup)
#> Chain 1: Iteration: 10 / 100 [ 10%]  (Warmup)
#> Chain 1: Iteration: 20 / 100 [ 20%]  (Warmup)
#> Chain 1: Iteration: 30 / 100 [ 30%]  (Warmup)
#> Chain 1: Iteration: 40 / 100 [ 40%]  (Warmup)
#> Chain 1: Iteration: 50 / 100 [ 50%]  (Warmup)
#> Chain 1: Iteration: 51 / 100 [ 51%]  (Sampling)
#> Chain 1: Iteration: 60 / 100 [ 60%]  (Sampling)
#> Chain 1: Iteration: 70 / 100 [ 70%]  (Sampling)
#> Chain 1: Iteration: 80 / 100 [ 80%]  (Sampling)
#> Chain 1: Iteration: 90 / 100 [ 90%]  (Sampling)
#> Chain 1: Iteration: 100 / 100 [100%]  (Sampling)
#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.017 seconds (Warm-up)
#> Chain 1:                0.015 seconds (Sampling)
#> Chain 1:                0.032 seconds (Total)
#> Chain 1: 
#> Warning: The largest R-hat is 1.22, indicating chains have not mixed.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#r-hat
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess

summary(bvar_obj)
#> Posterior mean estimates
#> ------------------------
#> 
#> 
#> beta
#> --------------------------------------------------------------------------------         
#>           Var1  Var2
#>   Var1.l1 0.01 -0.01
#>   Var2.l1 0.03  0.05
#> --------------------------------------------------------------------------------
#> 
#> 
#> Psi
#> --------------------------------------------------------------------------------      
#>        [,1]
#>   Var1 0.03
#>   Var2 0.00
#> --------------------------------------------------------------------------------
#> 
#> 
#> Sigma_u
#> --------------------------------------------------------------------------------      
#>        Var1 Var2
#>   Var1 3.59 0.09
#>   Var2 0.09 1.32
#> --------------------------------------------------------------------------------
#> 
# }