Performs VMA estimation using the conditional multivariate Gaussian likelihood function. This is the same function as VMA, with the likelihood function implemented in C++ for efficiency.

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`da` |
Data matrix of a k-dimensional VMA process with each column containing one time series |

`q` |
The order of VMA model |

`include.mean` |
A logical switch to include the mean vector. The default is to include the mean vector in estimation. |

`fixed` |
A logical matrix used to fix parameter to zero |

`beta` |
Parameter estimates for use in model simplification |

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

`prelim` |
A logical switch to select parameters to be included in estimation |

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

`thres` |
Threshold for t-ratio used to fix parameeter to zero. Deault is 2. |

`data` |
The data of the observed time series |

`MAorder` |
The VMA order |

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

`coef` |
Parameter estimates |

`secoef` |
Standard errors of the parameter estimates |

`residuals` |
Residual series |

`Sigma` |
Residual covariance matrix |

`Theta` |
The VAR coefficient matrix |

`mu` |
The constant vector |

`aic,bic` |
The information criteria of the fitted model |

Ruey S. Tsay

Tsay (2014, Chapter 3).

VMA

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