runningcovmat: Extract summary statistics for the posterior covariance...

In factorstochvol: Bayesian Estimation of (Sparse) Latent Factor Stochastic Volatility Models

Extract summary statistics for the posterior covariance matrix which have been stored during sampling

Description

runningcovmat extracts summary statistics from the model-implied covariance matrix from an fsvdraws object for one point in time.

Usage

runningcovmat(x, i, statistic = "mean", type = "cov")

Arguments

x	Object of class 'fsvdraws', usually resulting from a call of fsvsample.
i	A single point in time.
statistic	Indicates which statistic should be extracted. Defaults to 'mean'.
type	Indicates whether covariance (cov) or correlation (cor) should be extracted.

Value

Matrix containing the requested covariance matrix summary statistic.

See Also

Other extractors: cormat.fsvdraws(), covmat.fsvdraws(), runningcormat()

Examples

set.seed(1)
sim <- fsvsim(n = 500, series = 3, factors = 1) # simulate
res <- fsvsample(sim$y, factors = 1) # estimate

cov100mean <- runningcovmat(res, 100) # extract mean at t = 100
cov100sd <- runningcovmat(res, 100, statistic = "sd") # extract sd
lower <- cov100mean - 2*cov100sd
upper <- cov100mean + 2*cov100sd

true <- covmat(sim, 100) # true value

# Visualize mean +/- 2sd and data generating values
par(mfrow = c(3,3), mar = c(2, 2, 2, 2))
for (i in 1:3) {
 for (j in 1:3) {
  plot(cov100mean[i,j], ylim = range(lower, upper), pch = 3,
  main = paste(i, j, sep = ' vs. '), xlab = '', ylab = '')
  lines(c(1,1), c(lower[i,j], upper[i,j]))
  points(true[i,j,1], col = 3, cex = 2)
 }
}

factorstochvol documentation built on Nov. 24, 2023, 5:08 p.m.