There was an error in the previous code, this should be correct...
library('BB')
library('alabama')
library('nloptr')
r = rnorm(13551, 0.0004, 0.016) # pseudo returns
### Spline GARCH ###
### Specifying time trend
k = 3 # number of
knots
bounds = floor(1:k * 13551/k) # partition of time horizon
bounds = c(0, bounds[1:(k-1)])
time.lin = 0:13550 # linear time trend
time.nonlin <- matrix(rep(time.lin, k), length(time.lin), k) # quadratic
time trend
for(i in 1:k) { #
time.nonlin[,i] <- time.nonlin[,i] - bounds[i] #
time.nonlin[which(time.nonlin[,i] < 0), i] <- 0 #
time.nonlin[, i] <- time.nonlin[, i]^2 #
} #
time.trend = cbind(time.lin, time.nonlin)
for(i in 1:dim(time.trend)[2]) time.trend[,i] <-
time.trend[,i]/time.trend[dim(time.trend)[1], i] # normalizing between
0 and 1
head(time.trend)
tail(time.trend)
### Spline function
splgarch <- function(para) {
mu <- para[1]
omega <- para[2]
alpha <- para[3]
beta <- para[4]
cc <- para[5]
w <- para[6:(k+6)]
Tau <- cc * exp( apply(t(diag(w)%*%t(time.trend)), 1, sum) )
e2 <- (r-mu)^2
e2t <- omega + alpha * c(mean(e2), e2[-length(r)]) / Tau^2
s2 <- filter(e2t, beta, "recursive", init = mean(e2))
sig2 <- s2 * Tau
0.5*sum( log(2*pi) + log(sig2) + e2/sig2) }
### Spline parameter initialization
mu <- mean(r)
small <- 1e-6
alpha <- 0.1
beta <- 0.8
omega <- (1-alpha-beta)
para <- c(mu, omega, alpha, beta, 1, rep(small, length(4:(k+4))))
lo <- c(-10*abs(mu), small, small, small, rep(-10, length(3:(k+4))))
hi <- c(10*abs(mu), 100*abs(mu), 1-small, 1-small, rep(10, length(3:(k+4))))
### Spline optimization
fit <- nlminb(start = para, objective = splgarch, lower = lo, upper =
hi, hessian = TRUE, control = list(x.tol = 1e-8,trace=0))
names(fit$par) <- c('mu', 'omega', 'alpha', 'beta', 'c', paste('w', sep
= '', 0:k))
round(fit$par, 6)
fit.hessian = hessian(splgarch, fit$par, method="complex")
fit.hessian
Am 2/7/2014 10:28 AM, schrieb Paul Gilbert:
On 02/07/2014 08:19 AM, Bastian Offermann wrote:
Hi all,
I am currently implementing the Engle & Rangel (2008) Spline GARCH
model. I use the nlminb optimizer which does not provide a hessian
unfortunately to get the standard errors of the coefficients. I can get
around this using the 'hessian' function in numDeriv, but usually get
NaN values for the omega parameter.
Do you know why this happens, or can you provide a simple example? An
NaN value from hessian() is often because the function fails to
evaluate in a small neighbourhood of the point where it is being
calculated, that is, at your parameter estimate. Are you on the
boundary of the feasible region?
Can anybody recommend additional optimizers that directly return a
hessian?
A hessian returned by an optimizer is usually one that is built up by
some approximation during the optimization process. One of the
original purposes of hessian() was to try to do something that is
usually better than that, specifically because you want a good
approximation if you are going to use it to calculate standard errors.
(And, of course, you want the conditions to hold for the hessian to be
an approximation of the variance.) Just because an optimizer returns
something for the hessian, it it not clear that you would want to use
it to calculate standard errors. The purpose of the hessian built up
by an optimizer is to speed the optimization, not necessarily to
provide a good approximation to the hessian. In the case where
hessian() is returning NaNs I would be concerned that anything
returned by an optimizer could be simply bogus.
How sensitive are the coefficients to the initial starting values?
This depends on a number of things, the optimizer you use being one of
them. Most optimizers have some mechanism to specify something
different from the default for the stopping criteria and you can, for
a problem without local optimum issues (e.g. convex level sets),
reduce sensitivity to the starting value by tightening the stopping
criteria. The more serious problem is when you have local optimum
issues. Then you will get false convergence and thus extreme
sensitivity to starting values. Even for a parameter space that is
generally good, there are often parameter values for which the
optimization is a bit sensitive. And, of course, all this also depends
on your dataset. Generally, the sensitivity will increase with short
datasets.
The previous paragraph is about the coefficient estimate. At the same
coefficient estimate hessian() will return the same thing, but a
hessian built up by an optimizer will depend on the path, and
generally needs a fairly large number of final steps in the vicinity
of the optimum to give a good approximation. Thus, somewhat counter
intuitively, if you do an optimization starting with values for the
coefficients that are very close to the optimum you will get quick
convergence but often a bad hessian approximation from the optimizer.
Paul
Thanks in advance!
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