estBlackBox4 {dse1} | R Documentation

## Estimate a TSmodel

### Description

Estimate a TSmodel with Brute Force Technique.

### Usage

        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

    if(is.R()) data("eg1.DSE.data.diff", package="dse1")
    z <-  bft(eg1.DSE.data.diff)

[Package Contents]