Description Usage Arguments Details Value References See Also Examples
svlm
is a wrapper around svsample
with a formula interface.
The name derives from SV and lm
because a linear model with SV residuals is fitted.
The function simulates from the joint posterior distribution of the regression coefficients and the SV
parameters mu
, phi
, sigma
(and potentially nu
and rho
),
along with the latent logvolatilities h_0,...,h_n
and returns the
MCMC draws.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21  svlm(
formula,
data,
draws = 10000,
burnin = 1000,
heavytails = FALSE,
asymmetry = FALSE,
priorspec = NULL,
thin = 1,
keeptime = "all",
quiet = FALSE,
startpara = NULL,
startlatent = NULL,
parallel = c("no", "multicore", "snow"),
n_cpus = 1L,
cl = NULL,
n_chains = 1L,
print_progress = "automatic",
expert = NULL,
...
)

formula 
an object of class 
data 
an optional data frame, list or environment (or object
coercible by 
draws 
single number greater or equal to 1, indicating the number of draws after burnin (see below). Will be automatically coerced to integer. The default value is 10000. 
burnin 
single number greater or equal to 0, indicating the number of draws discarded as burnin. Will be automatically coerced to integer. The default value is 1000. 
heavytails 
if 
asymmetry 
if 
priorspec 
using the smart constructor 
thin 
single number greater or equal to 1, coercible to integer.
Every 
keeptime 
Either 'all' (the default) or 'last'. Indicates which latent volatility draws should be stored. 
quiet 
logical value indicating whether the progress bar and other
informative output during sampling should be omitted. The default value is

startpara 
optional named list, containing the starting values
for the parameter draws. If supplied, 
startlatent 
optional vector of length 
parallel 
optional one of 
n_cpus 
optional positive integer, the number of CPUs to be used in case of
parallel computations. Defaults to 
cl 
optional socalled SNOW cluster object as implemented in package

n_chains 
optional positive integer specifying the number of independent MCMC chains 
print_progress 
optional one of 
expert 
optional named list of expert parameters. For most
applications, the default values probably work best. Interested users are
referred to the literature provided in the References section. If

... 
Any extra arguments will be forwarded to

For details concerning the algorithm please see the paper by Kastner and FrühwirthSchnatter (2014) and Hosszejni and Kastner (2019).
The value returned is a list object of class svdraws
holding
para 

latent 

latent0 

tau 

beta 

y 
the left hand side of the observation equation, usually
the argument 
runtime 

priors 
a 
thinning 

summary 

meanmodel 

svlm 
a flag for the use of 
model_terms 
helper object that represents the formula 
formula 
argument 
xlevels 
helper object that is needed to interpret the formula 
To display the output, use print
, summary
and plot
. The
print
method simply prints the posterior draws (which is very likely
a lot of output); the summary
method displays the summary statistics
currently stored in the object; the plot
method
plot.svdraws
gives a graphical overview of the posterior
distribution by calling volplot
, traceplot
and
densplot
and displaying the results on a single page.
Kastner, G. and FrühwirthSchnatter, S. (2014). Ancillaritysufficiency interweaving strategy (ASIS) for boosting MCMC estimation of stochastic volatility models. Computational Statistics & Data Analysis, 76, 408–423, doi: 10.1016/j.csda.2013.01.002.
Hosszejni, D. and Kastner, G. (2019). Approaches Toward the Bayesian Estimation of the Stochastic Volatility Model with Leverage. Springer Proceedings in Mathematics & Statistics, 296, 75–83, doi: 10.1007/9783030306113_8.
svsample
, svsim
, specify_priors
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16  # Simulate data
n < 50L
dat < data.frame(x = runif(n, 3, 4),
z = runif(n, 1, 0.5))
designmatrix < matrix(c(dat$x, dat$x^2, log10(dat$x),
dat$z), ncol = 4)
betas < matrix(c(1, 1, 2, 0), ncol = 1)
y < designmatrix %*% betas + svsim(n)$y
dat$y < y
# Formula interface
res < svlm(y ~ 0 + x + I(x^2) + log10(x) + z, data = dat)
# Prediction
predn < 10L
preddat < data.frame(x = runif(predn, 3, 4),
z = runif(predn, 1, 0.5))
pred < predict(res, newdata = preddat, steps = predn)

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.