"rugarch" and external regressors

Hi Alexios,

thanks for your quick and extremely helpful response (and for your
beautiful package).

You were right, the nrow() of both objects weren't equal (if anybody has
a similar problem and finds this post in the mailing list while
searching for help, here is my mistake: nrow(ts.rtr.zoo) was the length
of the time series, while nrow(as.matrix(df.merged.__imputed[1,c(4, 9,
11)])) was the number of time series itself).

Sorry for bothering you again, but i have some troubles fitting the
model. The estimates for my external regressors seem to be very close to
zero (in fact, all digits that are displayed are zero) and it happens
quite often that ugarchfit cannot invert hessian.
I have seen that it is possible and helpful to manipulate the solver
using solver.control in order to get a better fit of the models, but i
wonder if there are any rules of thumb for some cases? (I want to
estimate parameters for 225 different univariate time series which i
believe to be quite similar, so simply playing around with
solver.control for every time series until it fits the data will be
quite painful.) I've noticed that adjusting the tolerance parameter
"tol" was helpful sometimes, but not in every single case. Is there some
information in the data itself which tells me what kind of adjustments
for the solver might be most helpful?

Best regards,
Max


2013/3/3 alexios ghalanos <alexios at 4dscape.com <mailto:alexios at 4dscape.com>>

    On 03/03/2013 17:58, Max Alletsee wrote:

        Hi,

        i'm trying to fit some GARCH models with external regressors
        using the
        package "rugarch", but it keeps on failing...

        This is how I create my zoo-object:

            ts.rtr <-
            ts(data=as.numeric(df.merged.__imputed[1,32:5525]), start=1,
            end=5494, frequency=1)
            ts.rtr.zoo <- as.zoo(ts.rtr)



        This is the specification of my model:

               spec <- ugarchspec(variance.model = list(model="sGARCH",
            garchOrder =
            c(1,1),
            external.regressors=as.matrix(__df.merged.imputed[1,c(4, 9,
            11)])))


    Run:
    'NROW(as.matrix(df.merged.__imputed[1,c(4, 9, 11)])))'.

    How many rows does that report? Are they the same as NROW(ts.rtr.zoo)?



        This is my attempt to fit the model:

            fit <- ugarchfit(spec = spec, data = ts.rtr.zoo, solver =
            "hybrid")



        It always stops with the error message:

            Error in .sgarchfit(spec = spec, data = data, out.sample =
            out.sample,  :


            Subscript out of bounds


    -Alexios