Hi
Could someone explain to a beginner in Bayes statistics/MCMC/Winbugs why
WinBugs is faster in estimating a stochastic volatility time series
model than a GARCH model, for example?
Estimating a GARCH model with the following code is prohibitively slow
for n ~ 1000, while a stochastic volatility model, where the assignment
in (1) is replaced with a distribution specification, is much faster.
TIA for any help.
Regards,
Stephan
model garch;
{
mu ~ dlnorm(-2.3,0.2);
alpha ~ dlnorm(-2.0, 0.2);
beta ~ dlnorm(-0.2, 0.2);
theta[1] ~ dlnorm(-2.3,0.2);
for (t in 2:n) {
theta[t] <- mu + alpha * pow(y[t-1],2) + beta * theta[t-1]; #(1)
}
for (t in 1:n) {
yisigma2[t] <- 1/theta[t];
y[t] ~ dnorm(0,yisigma2[t]);
}
}
list(y = c(-0.320221363079782, 1.46071929942995, -0.408629619810947,
1.06096027386685,
1.71288920763163, 0.404314365893326, -0.905699012715806,
-1.01657225575983, -0.260349143819501, 0.142923934468979,
... ), n = 945)
-------------------------------------------------------------------
This list is for discussion of modelling issues and the BUGS software.
For help with crashes and error messages, first mail [log in to unmask]
To mail the BUGS list, mail to [log in to unmask]
Before mailing, please check the archive at www.jiscmail.ac.uk/lists/bugs.html
Please do not mail attachments to the list.
To leave the BUGS list, send LEAVE BUGS to [log in to unmask]
If this fails, mail [log in to unmask], NOT the whole list
|