Description Usage Arguments Details Value Author(s) References Examples

Performs simulation of a given VARMA model

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`nobs` |
Sample size |

`arlags` |
The exact lags of the VAR matrix polynomial. |

`malags` |
The exact lags of the VMA matrix polynomial. |

`cnst` |
Constant vector, Phi0 |

`phi` |
Matrix of VAR coefficient matrices in the order of the given arlags. |

`theta` |
Matrix of VMA coefficient matrices in the order of the given malags. |

`skip` |
The number of initial data to be omitted. Default is 200. |

`sigma` |
Covariance matrix (k-by-k, positive definite) of the innovations |

Use multivariate Gaussian distribution to generate random shocks. Then, generate a given VARMA model. The first skip data points were discarded.

`series` |
Generated series |

`noises` |
The noise series |

Ruey S. Tsay

Tsay (2014, Chapter 3). Multivariate Time Series Analysis with R and Financial Applications. John Wiley. Hoboken, NJ.

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