Estimate a TSmodel

Description

Estimate a TSmodel with Brute Force Technique.

Usage

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    estBlackBox4(data, estimation="estVARXls", 
                lag.weight=1.0,  variable.weights=1, 
                reduction="MittnikReduction", 
                criterion="taic", 
                trend=FALSE, subtract.means=FALSE,  re.add.means=TRUE, 
                standardize=FALSE, verbose=TRUE, max.lag=12, sample.start=10, warn=TRUE)
    bft(data, ... )

Arguments

data

A TSdata object.

estimation

a character string indicating the estimation method to use.

lag.weight

weighting to apply to lagged observations.

variable.weights

weighting to apply to series if estimation method is estWtVariables.

reduction

character string indicating reduction procedure to use.

criterion

criterion to be used for model selection. see informationTestsCalculations.

trend

if TRUE include a trend in the model.

subtract.means

if TRUE the mean is subtracted from the data before estimation.

re.add.means

if subtract.means is TRUE then if re.add.means is T the estimated model is converted back to a model for data without the mean subtracted.

standardize

if TRUE the data is transformed so that all variables have the same variance.

verbose

if TRUE then additional information from the estimation and reduction procedures is printed.

max.lag

VAR estimation is done for each lag up to max.lag.

sample.start

the starting point to use for calculating information criteria in the final selection.

warn

logical indicating if warning messages should be suppressed.

...

arguments passed to estBlackBox4.

Details

For each lag up to max.lag a VAR model is estimated and then a reduction procedure applied to select the best reduced model. Finally the best of the best reduced models is selected. The default estimation procedure is least squares estimation of the VAR models. This procedure is described as the brute force technique (bft) in Gilbert (1995).

Value

A TSestModel.

References

Gilbert, P.D. (1995) Combining VAR Estimation and State Space Model Reduction for Simple Good Predictions J. of Forecasting: Special Issue on VAR Modelling, 14, 229–250.

See Also

estBlackBox1, estBlackBox2 estBlackBox3 informationTestsCalculations

Examples

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data("eg1.DSE.data.diff", package="dse")
z <-  bft(eg1.DSE.data.diff)

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