# predcov: Predicts covariance matrix In factorstochvol: Bayesian Estimation of (Sparse) Latent Factor Stochastic Volatility Models

## Description

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

## Usage

 `1` ```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.

Other predictors: `predcond`, `predcor`, `predh`, `predloglikWB`, `predloglik`, `predprecWB`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20``` ```## Not run: 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) ## End(Not run) ```