I'm trying to replicate in R some results that I get from an Eviews estimation
of a model with GARCH errors. For this, I'm using the "rugarch" package. The
problem is that estimated parameters differ by a lot. This is the set up I'm
using in R (the data are in an xts object names "datos"):
spec <- ugarchspec(
variance.model = list(model = "sGARCH", garchOrder = c(2, 1)),
mean.model = list(armaOrder = c(2, 0), include.mean = TRUE,
external.regressors = as.matrix(datos[, c("jueves", "viernes", "sem2",
"sem2_ene", "sem3_nov", "sem4_nov")]))
)
fit_garch <- ugarchfit(spec = spec, data = datos$d_TC, solver = "gosolnp")
This es the set up I'm using in Eviews:
equation fit_garch.arch(2,1) d_TC c ar(1) ar(2) jueves viernes sem2 sem2_ene
sem3_nov sem4_nov
As you can see, they are supposed to be the same model, an ARMA(2,0) with
constant term plus some "external regressors" in the mean equation, and a
GARCH(2,1) models for the variance. But coefficient estimates differ by no
small amount.
I verified that data are the same (run OLS and got identical results between
the softwares). As a side note, R results does not converge easily, I tried
some of the solvers and only "gosolnp" and "hybrid" converge.
This is my first time using "rugarch", so chances are big that there is
something wrong in my R set up.
Any help will be greatly appreciated.
Regards.
Manfred E.
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