genMineData: Generate Data

Description

Generate data for Monte Carlo experiments

Usage

genMineData(umodel, ymodel, uinput=NULL, sampleT=100, 
      unoise=NULL, usd=1,ynoise=NULL, ysd=1, rng=NULL)
    build.input.models(data, max.lag=NULL)
    build.diagonal.model(multi.models)

Value

A TSdata object.

Arguments

umodel: Model for input data.
ymodel: Model for output data.
sampleT: Number of periods of data to generate.
unoise: Input noise.
usd: Standard deviationof input noise.
ynoise: Output noise.
ysd: Standard deviation of output noise.
rng: RNG setting.
multi.models: A list of TSestModels.
data: data from which to build models.
max.lag: number of lags in the estimated models.
uinput: Input data to umodel.

Details

This function generates test data using specified models. umodel is used to generate data corresponding to input data and ymodel is used to generate data corresponding to output data. The result of umodel is used as input to ymodel so the input dimension of ymodel should be the output dimension of umodel. Typically the ymodel would be degenerate in some of the input variables so the effective inputs are a subset. If umodel requires input data it should be specified in uinput If noise is NULL then an normal noise will be generated by simulate. This will be iid N(0,I). The RNG will be set first to rng if it is specified. If unoise or ynoise are specified they should be as expected by simulate for the specified umodel and ymodel.

genMineData uses build.input.models, which makes a list of univariate TSestModels, one for each series in inputData(data) estimated by estVARXls with max.lag lags. genMineData then uses build.diagonal.model which builds one diagonal model from a list of models returned by build.input.models. It uses the AR part only.

Examples

Run this code

    data("eg1.DSE.data.diff", package="dse")
    umodel <- build.diagonal.model(
            build.input.models(eg1.DSE.data.diff, max.lag=2))
    z  <- TSdata(output=outputData(eg1.DSE.data.diff), 
                 input = inputData(eg1.DSE.data.diff))
    ymodel <- TSmodel(estVARXls(z, max.lag=3))   
    sim.data <- genMineData(umodel, ymodel)