IRF_Plots {BHSBVAR}

Plot Impulse Responses

Description

Plot Impulse Responses.

Usage

IRF_Plots(results, varnames, shocknames = NULL, xlab = NULL, ylab = NULL)

Arguments

results

List containing the results from running BH_SBVAR().

varnames

Character vector containing the names of the endogenous variables.

shocknames

Character vector containing the names of the shocks.

xlab

Character label for the horizontal axis of impulse response plots (default = NULL). Default produces plots without a label for the horizontal axis.

ylab

Character label for the vertical axis of impulse response plots (default = NULL). Default produces plots without a label for the vertical axis.

Details

Plots impulse responses and returns a list containing the actual processed data used to create the plots.

Value

A list containing impulse responses.

Author(s)

Paul Richardson

Examples

# Import data
library(BHSBVAR)
set.seed(123)
data(USLMData)
y0 <- matrix(data = c(USLMData$Wage, USLMData$Employment), ncol = 2)
y <- y0 - (matrix(data = 1, nrow = nrow(y0), ncol = ncol(y0)) %*% 
             diag(x = colMeans(x = y0, na.rm = FALSE, dims = 1)))
colnames(y) <- c("Wage", "Employment")

# Set function arguments
nlags <- 8
itr <- 5000
burn <- 0
thin <- 1
acc <- TRUE
h <- 20
cri <- 0.95

# Priors for A
pA <- array(data = NA, dim = c(2, 2, 8))
pA[, , 1] <- c(0, NA, 0, NA)
pA[, , 2] <- c(1, NA, -1, NA)
pA[, , 3] <- c(0.6, 1, -0.6, 1)
pA[, , 4] <- c(0.6, NA, 0.6, NA)
pA[, , 5] <- c(3, NA, 3, NA)
pA[, , 6] <- c(NA, NA, NA, NA)
pA[, , 7] <- c(NA, NA, 1, NA)
pA[, , 8] <- c(2, NA, 2, NA)

# Position priors for Phi
pP <- matrix(data = 0, nrow = ((nlags * ncol(pA)) + 1), ncol = ncol(pA))
pP[1:nrow(pA), 1:ncol(pA)] <-
  diag(x = 1, nrow = nrow(pA), ncol = ncol(pA))

# Confidence in the priors for Phi
x1 <- 
  matrix(data = NA, nrow = (nrow(y) - nlags), 
         ncol = (ncol(y) * nlags))
for (k in 1:nlags) {
  x1[, (ncol(y) * (k - 1) + 1):(ncol(y) * k)] <-
    y[(nlags - k + 1):(nrow(y) - k),]
}
x1 <- cbind(x1, 1)
colnames(x1) <- 
  c(paste(rep(colnames(y), nlags),
          "_L",
          sort(rep(seq(from = 1, to = nlags, by = 1), times = ncol(y)),
               decreasing = FALSE),
          sep = ""),
    "cons")
y1 <- y[(nlags + 1):nrow(y),]
ee <- matrix(data = NA, nrow = nrow(y1), ncol = ncol(y1))
for (i in 1:ncol(y1)) {
  xx <- cbind(x1[, seq(from = i, to = (ncol(x1) - 1), by = ncol(y1))], 1)
  yy <- matrix(data = y1[, i], ncol = 1)
  phi <- solve(t(xx) %*% xx, t(xx) %*% yy)
  ee[, i] <- yy - (xx %*% phi)
}
somega <- (t(ee) %*% ee) / nrow(ee)
lambda0 <- 0.2
lambda1 <- 1
lambda3 <- 100
v1 <- matrix(data = (1:nlags), nrow = nlags, ncol = 1)
v1 <- v1^((-2) * lambda1)
v2 <- matrix(data = diag(solve(diag(diag(somega)))), ncol = 1)
v3 <- kronecker(v1, v2)
v3 <- (lambda0^2) * rbind(v3, (lambda3^2))
v3 <- 1 / v3
pP_sig <- diag(x = 1, nrow = nrow(v3), ncol = nrow(v3))
diag(pP_sig) <- v3

# Confidence in long-run restriction priors
pR_sig <-
  array(data = 0,
        dim = c(((nlags * ncol(y)) + 1),
                ((nlags * ncol(y)) + 1),
                ncol(y)))
Ri <-
  cbind(kronecker(matrix(data = 1, nrow = 1, ncol = nlags),
                  matrix(data = c(1, 0), nrow = 1)),
        0)
pR_sig[, , 2] <- (t(Ri) %*% Ri) / 0.1

# Confidence in priors for D
kappa1 <- matrix(data = 2, nrow = 1, ncol = ncol(y))

# Set graphical parameters
par(cex.axis = 0.8, cex.main = 1, font.main = 1, family = "serif",
    mfrow = c(2, 2), mar = c(2, 2.2, 2, 1), las = 1)

# Estimate the parameters of the model
results1 <- 
  BH_SBVAR(y = y, nlags = nlags, pA = pA, pP = pP, pP_sig = pP_sig,
           pR_sig = pR_sig, kappa1 = kappa1, itr = itr, burn = burn,
           thin = thin, cri = cri)
           
irf <- IRF(results = results1, h = h, acc = acc, cri = cri)

# Plot impulse responses
varnames <- colnames(USLMData)[2:3]
shocknames <- c("Labor Demand","Labor Supply")
irf_results <- 
  IRF_Plots(results = irf, varnames = varnames,
            shocknames = shocknames)