dlmRandom: Random DLM

In dlm: Bayesian and Likelihood Analysis of Dynamic Linear Models

Random DLM

Description

Generate a random (constant or time-varying) object of class "dlm", along with states and observations from it.

Usage

dlmRandom(m, p, nobs = 0, JFF, JV, JGG, JW)

Arguments

m	dimension of the observation vector.
p	dimension of the state vector.
nobs	number of states and observations to simulate from the model.
JFF	should the model have a time-varying FF component?
JV	should the model have a time-varying V component?
JGG	should the model have a time-varying GG component?
JW	should the model have a time-varying W component?

Details

The function generates randomly the system and observation matrices and the variances of a DLM having the specified state and observation dimension. The system matrix GG is guaranteed to have eigenvalues strictly less than one, which implies that a constant DLM is asymptotically stationary. The default behavior is to generate a constant DLM. If JFF is TRUE then a model for nobs observations in which all the elements of FF are time-varying is generated. Similarly with JV, JGG, and JW.

Value

The function returns a list with the following components.

mod	An object of class "dlm".
theta	Matrix of simulated state vectors from the model.
y	Matrix of simulated observations from the model.

If nobs is zero, only the mod component is returned.

Author(s)

Giovanni Petris [e-mail]

References

Anderson and Moore, Optimal filtering, Prentice-Hall (1979)

See Also

dlm

Examples

dlmRandom(1, 3, 5)

dlm documentation built on Sept. 21, 2024, 9:06 a.m.