% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/summary.R
\name{summary.bvar}
\alias{summary.bvar}
\title{Summarise a fitted steady-state BVAR model}
\usage{
\method{summary}{bvar}(object, pars = NULL, stat = "mean", t = NULL, ...)
}
\arguments{
\item{object}{A steady-state \code{bvar} object that has been passed through
\code{\link{fit}}.}

\item{pars}{Character vector of parameter names to include. If
\code{NULL} (default), all available parameters are displayed.}

\item{stat}{Character. Posterior summary statistic to display:
\code{"mean"} (default) or \code{"median"}.}

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

\item{...}{Additional arguments (currently unused).}
}
\value{
Returns the input object invisibly.
}
\description{
Computes and prints posterior summaries from a fitted steady-state \code{bvar} object.
The printed output depends on whether the model is homoscedastic or includes
stochastic volatility (RW or AR1 specification).
}
\details{
The function summarises the following estimated parameters:
\itemize{
\item \code{beta}: \eqn{kp \times k} VAR coefficient matrix
\item \code{Psi}: \eqn{k \times q} steady-state parameter matrix
\item \code{Sigma_u}: innovation covariance matrix (\eqn{k \times k} for homoscedastic,
\eqn{T \times k \times k} for stochastic volatility)
\item If Random Walk stochastic volatility:
\itemize{
\item \code{A}: \eqn{k \times k} lower triangular matrix with ones on the diagonal that describes
the contemporaneous interaction of the endogenous variables
\item \code{phi}: \eqn{k}-dimensional vector of log volatility innovation variances
}
\item If AR1 stochastic volatility:
\itemize{
\item \code{A}: \eqn{k \times k} lower triangular matrix with ones on the diagonal that describes
the contemporaneous interaction of the endogenous variables
\item \code{gamma_0}: \eqn{k}-dimensional vector of log volatility intercepts
\item \code{gamma_1}: \eqn{k}-dimensional vector of log volatility slopes
\item \code{Phi}: \eqn{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)

summary(bvar_obj)
}
}
