Description Usage Arguments Value Author(s) References Examples
This function estimates ARMA-GARCH/APARCH models with varying order and returns the one with the smallest goodness-of-fit criteria
1 2 3 4 5 6 | gsSelect(data, order.max = c(1, 1, 1, 1),
selection.criteria = c("AIC", "AICc", "BIC"), is.aparch = FALSE,
cond.dist = c("stableS0", "stableS1", "stableS2", "gev",
"gat", "norm", "std", "sstd", "skstd", "ged"),
include.mean = TRUE,
algorithm = c("sqp", "sqp.restriction", "nlminb", "nlminb+nm"), ...)
|
data |
Data with the time series to be estimated. It must be a numeric vector not contain NA, NULL or Inf values. |
order.max |
Maximum order of models to search. It must by a vector of the type |
selection.criteria |
The goodness-of-fit criterion to be used when searching for the best model. Three different criterias are allowed: AIC, AICc or BIC. For more information see Brockwell and Davis (1996). |
is.aparch |
Boolean variable indicating whether to search for ARMA-GARCH or ARMA-APARCH models. |
cond.dist |
a character string naming conditional distribution of innovations. The package was created to accept the following distributions: |
include.mean |
This is a boolean variable. It intercept is TRUE than we estimate the model with intercept. |
algorithm |
The algorithm to be used to search for the optimum value. The current version of the GEVStableGarch package implements
four different optimization procedures, namely the |
... |
Additional parameters to be passed to function |
Returns a S4 object of class "GEVSTABLEGARCH" with the best model. See GEVSTABLEGARCH-class
for details.
Thiago do Rego Sousa.
Brockwell, P.J., Davis, R.A. (1996). Introduction to Time Series and Forecasting. Springer, New York.
1 2 3 4 5 6 7 | # Best ARMA-GARCH model within the range ARMA(0,0)-GARCH(1,0) to ARMA(0,0)-GARCH(1,1)
# using the Corrected Akaike Information Criteria (AICc)
data(dem2gbp)
x = dem2gbp[,1]
model = gsSelect (data = x, order.max = c(0,0,1,1), is.aparch = FALSE,
algorithm = "sqp", cond.dist = "gev", selection.criteria = "AIC",
include.mean = FALSE)
|
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