svsample.svsample2 is a minimal overhead version of svsample
with slightly different default arguments and a simplified return value
structure. It is intended to be used mainly for one-step updates where speed
is an issue, e.g., as a plug-in into other MCMC samplers. Note that
absolutely no input checking is performed, thus this function is to be used
with proper care!
svsample2(y, draws = 1, burnin = 0, priormu = c(0, 100),
priorphi = c(5, 1.5), priorsigma = 1, priornu = NA,
priorlatent0 = "stationary", thinpara = 1, thinlatent = 1,
thintime = NULL, keeptime = "all", keeptau = FALSE, quiet = TRUE,
startpara, startlatent)numeric vector containing the data (usually log-returns), which must not contain zeroes.
single number greater or equal to 1, indicating the number of draws after burn-in (see below). Will be automatically coerced to integer. The defaults value is 1.
single number greater or equal to 0, indicating the number of draws discarded as burn-in. Will be automatically coerced to integer. The default value is 0.
numeric vector of length 2, indicating mean and standard
deviation for the Gaussian prior distribution of the parameter mu,
the level of the log-volatility. The default value is c(0, 100),
which constitutes a practically uninformative prior for common exchange rate
datasets, stock returns and the like.
numeric vector of length 2, indicating the shape parameters
for the Beta prior distribution of the transformed parameter
(phi + 1) / 2, where phi denotes the persistence of the
log-volatility. The default value is c(5, 1.5), which constitutes a
prior that puts some belief in a persistent log-volatility but also
encompasses the region where phi is around 0.
single positive real number, which stands for the scaling
of the transformed parameter sigma^2, where sigma denotes the
volatility of log-volatility. More precisely, sigma^2 ~ priorsigma *
chisq(df = 1). The default value is 1, which constitutes a
reasonably vague prior for many common exchange rate datasets, stock returns
and the like.
numeric vector of length 2 (or NA), indicating the
lower and upper bounds for the uniform prior distribution of the parameter
nu, the degrees-of-freedom parameter of the conditional innovations
t-distribution. The default value is NA, fixing the
degrees-of-freedom to infinity. This corresponds to conditional standard
normal innovations, the pre-1.1.0 behavior of stochvol.
either a single non-negative number or the string
'stationary' (the default, also the behavior before version 1.3.0).
When priorlatent0 is equal to 'stationary', the stationary
distribution of the latent AR(1)-process is used as the prior for the
initial log-volatility h_0. When priorlatent0 is equal to a
number \(B\), we have \(h_0 \sim N(\mu, B\sigma^2)\) a priori.
single number greater or equal to 1, coercible to integer.
Every thinparath parameter draw is kept and returned. The default
value is 1, corresponding to no thinning of the parameter draws -- every
draw is stored.
single number greater or equal to 1, coercible to integer.
Every thinlatentth latent variable draw is kept and returned. The
default value is 1, corresponding to no thinning of the latent variable
draws, i.e. every draw is kept.
Deprecated. Use 'keeptime' instead.
Either 'all' (the default) or 'last'. Indicates which latent
logical value indicating whether the 'variance inflation factors' should be stored (used for the sampler with conditional t innovations only). This may be useful to check at what point(s) in time the normal disturbance had to be 'upscaled' by a mixture factor and when the series behaved 'normally'.
logical value indicating whether the progress bar and other
informative output during sampling should be omitted. The default value is
TRUE, implying non-verbose output.
compulsory named list, containing the starting
values for the parameter draws. startpara must contain three elements
named mu, phi, and sigma, where mu is an
arbitrary numerical value, phi is a real number between -1 and
1, and sigma is a positive real number. Moreover, if
priornu is not NA, startpara must also contain an
element named nu (the degrees of freedom parameter for the
t-innovations).
compulsory vector of length length(x$y),
containing the starting values for the latent log-volatility draws.
A list with three components:
3 times
draws matrix containing the parameter draws. If priornu is not
NA, this is a 4 times draws matrix.
length(y) times draws matrix containing draws of
the latent variables h_1, …{}, h_n.
Vector of
length draws containing the draw(s) of the initial latent variable
h_0.
Expert use only! For most applications, the use of
svsample is recommended.
As opposed to the ordinary svsample, the default values differ
for draws, burnin, and quiet. Note that currently
neither expert nor …{} arguments are provided.
# NOT RUN {
data(exrates)
aud.price <- subset(exrates,
as.Date("2010-01-01") <= date & date < as.Date("2011-01-01"),
"AUD")[,1]
draws <- svsample2(logret(aud.price),
draws = 10, burnin = 0,
startpara = list(phi = 0.95, mu = -10, sigma = 0.2, rho = -0.1),
startlatent = rep_len(-10, length(aud.price) - 1))
# }
Run the code above in your browser using DataLab