predcov: Predicts covariance matrix

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

Predicts covariance matrix

Description

predcov simulates from the posterior predictive distribution of the model-implied covariance matrix.

Usage

predcov(x, ahead = 1, each = 1)

Arguments

x	Object of class 'fsvdraws', usually resulting from a call to fsvsample.
ahead	Vector of timepoints, indicating how many steps to predict ahead.
each	Single integer (or coercible to such) indicating how often should be drawn from the posterior predictive distribution for each draw that has been stored during MCMC sampling.

Value

4-dimensional array containing draws from the predictive covariance distribution.

Note

Currently crudely implemented as a triple loop in pure R, may be slow.

See Also

Other predictors: predcond(), predcor(), predh(), predloglikWB(), predloglik(), predprecWB()

Examples

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

# Predict 1, 10, and 100 days ahead:
predobj <- predcov(res, ahead = c(1, 10, 100))

# Trace plot of draws from posterior predictive distribution
# of the covariance of Sim1 and Sim2:
# (one, ten, and 100 days ahead):
plot.ts(predobj[1,2,,])

# Smoothed kernel density estimates of predicted covariance
# of Sim1 and Sim2:
plot(density(predobj[1,2,,"1"], adjust = 2))
lines(density(predobj[1,2,,"10"], adjust = 2), col = 2)
lines(density(predobj[1,2,,"100"], adjust = 2), col = 3)

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