bvartools (version 0.0.1)

plot.bvarprd: Plotting Forecasts of BVAR Models

Description

A plot function for objects of class "bvarprd".

Usage

# S3 method for bvarprd
plot(x, ...)

Arguments

x

an object of class "bvarprd", usually, a result of a call to predict.bvar.

...

further graphical parameters.

Examples

# NOT RUN {
data("e1")
e1 <- diff(log(e1))

data <- gen_var(e1, p = 2, deterministic = "const")

y <- data$Y[, 1:73]
x <- data$Z[, 1:73]

set.seed(1234567)

iter <- 500 # Number of iterations of the Gibbs sampler
# Chosen number of iterations should be much higher, e.g. 30000.

burnin <- 100 # Number of burn-in draws
store <- iter - burnin

t <- ncol(y) # Number of observations
k <- nrow(y) # Number of endogenous variables
m <- k * nrow(x) # Number of estimated coefficients

# Set (uninformative) priors
a_mu_prior <- matrix(0, m) # Vector of prior parameter means
a_v_i_prior <- diag(0, m) # Inverse of the prior covariance matrix

u_sigma_df_prior <- 0 # Prior degrees of freedom
u_sigma_scale_prior <- diag(0, k) # Prior covariance matrix
u_sigma_df_post <- t + u_sigma_df_prior # Posterior degrees of freedom

# Initial values
u_sigma_i <- diag(.00001, k)
u_sigma <- solve(u_sigma_i)

# Data containers for posterior draws
draws_a <- matrix(NA, m, store)
draws_sigma <- matrix(NA, k^2, store)

# Start Gibbs sampler
for (draw in 1:iter) {
  # Draw conditional mean parameters
  a <- post_normal(y, x, u_sigma_i, a_mu_prior, a_v_i_prior)

# Draw variance-covariance matrix
u <- y - matrix(a, k) %*% x # Obtain residuals
u_sigma_scale_post <- solve(u_sigma_scale_prior + tcrossprod(u))
u_sigma_i <- matrix(rWishart(1, u_sigma_df_post, u_sigma_scale_post)[,, 1], k)
u_sigma <- solve(u_sigma_i) # Invert Sigma_i to obtain Sigma

# Store draws
if (draw > burnin) {
  draws_a[, draw - burnin] <- a
  draws_sigma[, draw - burnin] <- u_sigma
  }
}

# Generate bvar object
bvar_est <- bvar(y = y, x = x, A = draws_a[1:18,],
                 C = draws_a[19:21, ], Sigma = draws_sigma)

# Generate forecasts
bvar_pred <- predict(bvar_est, n.ahead = 10, new_D = rep(1, 10))

# Plot forecasts
plot(bvar_pred)

# }