Estimated Time Series Model

Description

Object containing a time series model, data, and estimation information.

Usage

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    TSestModel(obj)
    ## S3 method for class 'TSestModel'
TSestModel(obj)
    is.TSestModel(obj)

Arguments

obj

in the first usage an object from which a TSestModel object can be extracted (or constructed).

Details

The TSestModel class of objects are generated by estimation methods. See, for example, estVARXls. They contains a time series model (TSmodel), data (TSdata), and information obtained by evaluating the model with the data in an element called estimates containing:

like

The negative log likelihood function value (a vector of the total, constant, the det part, and the cov part)

cov

The estimated residual covariance.

pred

The one step ahead predictions (see predictT below). These are aligned with output data so that residuals are pred[1:sampleT,] - output[1:sampleT,]

sampleT

The end of the period (starting from 1) for which output is used for calculating one step ahead predictions.

predictT

The end of the period for which the model is simulated. sampleT must be less than or equal predictT. If predictT is greater than sampleT then each step ahead beyond sampleT is based on the prediction of the previous step and not corrected by the prediction error.

The element estimates may optionally also contain and element filter which may have

state

The one step ahead (filter) estimate of the state E[z(t)|y(t-1), u(t)]. Note: In the case where there is no input u this corresponds to what would usually be called the predicted state - not the filtered state.

track

The estimated state tracking error P(t|t-1). Again note, this corresponds to the predicted tracking eror not the filtered tracking error. This is NULL for innovations models.

smooth

a list of:

state

The smoother (two sided filter) estimate of the state E[z(t)| sampleT].

track

The smoothed estimate of the state tracking error P(t|sampleT). This is NULL for innovations models.

See Also

estVARXls, TSmodel, TSdata

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