estBlackBox3 {dse1} | R Documentation

## Estimate a TSmodel

### Description

Estimate a TSmodel.

### Usage

        estBlackBox3(data, estimation='estVARXls',
              lag.weight=1.0,
              reduction='MittnikReduction',
              criterion='aic',
              trend=FALSE,
              subtract.means=FALSE,  re.add.means=TRUE,
              standardize=FALSE, verbose=TRUE, max.lag=12, sample.start=10)

### Arguments

`data` | A TSdata object.
`estimation` | A character string indicating the estimation method to use.
`lag.weight` | Weighting to apply to lagged observations.
`reduction` | Character string indicating reduction procedure to use.
`criterion` | Criterion to be used for model selection. see `informationTestsCalculations`. taic might be a better default selection criteria but it is not available for ARMA models.
`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` | The number of lags to include in the VAR estimation.
`sample.start` | The starting point to use for calculating information criteria.

### Details

VAR models are estimated for each lag up to the specified max.lag. From these the best is selected according to the specified criteria. The reduction procedure is then applied to this best model and the best reduced model selected. The default estimation procedure is least squares estimation of a VAR model.

### Value

A TSestModel.

### See Also

`estBlackBox1`, `estBlackBox2` `estBlackBox4` `informationTestsCalculations`

### Examples

    if(is.R()) data("eg1.DSE.data.diff", package="dse1")
    z <-  estBlackBox3(eg1.DSE.data.diff)

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