tsDyn (version 0.9-1)

nlar: Non-linear time series model, base class definition

Description

Generic non-linear autogregressive model class constructor.

Usage

nlar(str, coefficients, fitted.values, residuals, k, model, model.specific=NULL, ...)

Arguments

str

a nlar.struct object, i.e. the result of a call to nlar.struct

coefficients, fitted.values, residuals, k, model, model.specific

internal structure

...

further model specific fields

Value

• An object of class nlar. nlar-methods for a list of available methods.

Details

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: [object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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.

References

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).

See Also

availableModels for currently available built-in models. nlar-methods for available nlar methods.