Description Usage Arguments Value Author(s) References See Also Examples

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 parameter to zero. Default 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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MTS documentation built on Oct. 10, 2018, 5:04 p.m.

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