makeTSnoise: Generate a random time series
In dse: Dynamic Systems Estimation (Time Series Package)

Description Usage Arguments Value

Generate a random time series (matrix). This is a utility typically used in a time series model simulate method and not called directly by the user.

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    makeTSnoise(sampleT,p,lags,noise=NULL, rng=NULL, Cov=NULL, sd=1, 
	     noise.model=NULL, noise.baseline=0,
             tf=NULL, start=NULL,frequency=NULL)

`sampleT`	an integer indicating the number of periods.
`p`	an integer indicating the number of series.
`lags`	an integer indicating the number of periods prior to the sample (initial data w0) for which random numbers should be generated. This is useful in ARMA models.
`noise`	Noise can be supplied. Otherwise it will be generated. If supplied it should be a list as described below under returned value.
`Cov`	The covariance of the noise process. If this is specified then sd is ignored. A vector or scalar is treated as a diagonal matrix. For an object of class TSestModel, if neither Cov nor sd are specified, then Cov is set to the estimated covariance (`model$estimates$cov`).
`sd`	The standard deviation of the noise. This can be a vector.
`noise.model`	A TSmodel to be used for generating noise (not yet supported by SS methods).
`noise.baseline`	a constant or matrix to be added to noise. Alternately this can be a vector of length p, each value of which is treated as a constant to add to the coresponding noise series.
`rng`	The random number generator information needed to regenerate a simulation.
`tf`	a time frame to use for the generated matrix. (alternately use start and frequency)
`start`	a time start date to use for the generated matrix.
`frequency`	a time frequency to use for the generated matrix.