Non-linear time series model, base class definition

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Description

Generic non-linear autogregressive model class constructor.

Usage

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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

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:

str

nlar.struct object, encapsulating general infos such as time series length, embedding parameters, forecasting steps, model design matrix

coefficients

a named vector of model estimated/fixed coefficients

k

total number of estimated coefficients

fitted.values

model fitted values

residuals

model residuals

model

data frame containing the variables used

model.specific

(optional) model specific additional infos

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.

Value

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

Author(s)

Antonio, Fabio Di Narzo

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.

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