# runningcovmat: Extract summary statistics for the posterior covariance... In factorstochvol: Bayesian Estimation of (Sparse) Latent Factor Stochastic Volatility Models

## Description

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

## Usage

 `1` ```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.

Other extractors: `cormat.fsvdraws`, `covmat.fsvdraws`, `runningcormat`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21``` ```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) } } ```