nlar {tsDyn} | R Documentation |
Generic non-linear autogregressive model class constructor.
nlar(str, coefficients, fitted.values, residuals, k, model.specific=NULL, ...)
str |
a nlar.struct object, i.e. the result of a call to nlar.struct |
coefficients, fitted.values, residuals, k, model.specific |
internal structure |
... |
further model specific fields |
Constructor for the generic nlar
model class.
On a fitted object you can call some generic methods. For a list of them, see nlar-methods
.
An object of the nlar
class is a list of (at least) components:
nlar.struct
object, encapsulating general infos such as time series
length, embedding parameters, forecasting steps, model design matrix
A nlar
object normally should also have a model-specific subclass (i.e., nlar
is a
virtual class).
Each subclass should define at least a print
and, hopefully, a oneStep
method, which is used by predict.nlar
to iteratively extend ahead the time series.
An object of class nlar
.
nlar-methods for a list of available methods.
Antonio, Fabio Di Narzo
Non-linear time series models in empirical finance, Philip Hans Franses and Dick van Dijk, Cambridge: Cambridge University Press (2000).
Non-Linear Time Series: A Dynamical Systems Approach, Tong, H., Oxford: Oxford University Press (1990).
availableModels
for currently available built-in models.
nlar-methods for available nlar
methods.