Performs conditional maximum likelihood estimation of a VARMA model. Multivariate Gaussian likelihood function is used. This is the same function as VARMA, with the likelihood function implemented in C++ for efficiency.

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`da` |
Data matrix (T-by-k) of a k-dimensional time series with sample size T. |

`p` |
AR order |

`q` |
MA order |

`include.mean` |
A logical switch to control estimation of the mean vector. Dafault is to include the mean in estimation. |

`fixed` |
A logical matrix to control zero coefficients in estimation. It is mainly used by the command refVARMA. |

`beta` |
Parameter estimates to be used in model simplification, if needed |

`sebeta` |
Standard errors of parameter estimates for use in model simplification |

`prelim` |
A logical switch to control preliminary estimation. Deafult is none. |

`details` |
A logical switch to control the amount of output. |

`thres` |
A threshold used to set zero parameter constraints based on individual t-ratio. Default is 2. |

The fixed command is used for model refinement

`data` |
Observed data matrix |

`ARorder` |
VAR order |

`MAorder` |
VMA order |

`cnst` |
A logical switch to include the mean vector |

`coef` |
Parameter estimates |

`secoef` |
Standard errors of the estimates |

`residuals` |
Residual matrix |

`Sigma` |
Residual covariance matrix |

`aic,bic` |
Information criteria of the fitted model |

`Phi` |
VAR coefficients |

`Theta` |
VMA coefficients |

`Ph0` |
The constant vector |

Ruey S. Tsay

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

VARMA

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