estBlackBox4 {dse} 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",
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).

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.

estBlackBox1, estBlackBox2 estBlackBox3 informationTestsCalculations
data("eg1.DSE.data.diff", package="dse")