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

1 |

`da` |
A T-by-k data matrix of a k-dimensional seasonal time series |

`order` |
Regular order (p,d,q) of the model |

`sorder` |
Seasonal order (P,D,Q) of the model |

`s` |
Seasonality. s=4 for quarterly data and s=12 for monthly series |

`include.mean` |
A logical switch to include the mean vector. Deafult is to include the mean |

`fixed` |
A logical matrix to set zero parameter constraints |

`details` |
A logical switch for output |

`switch` |
A logical switch to exchange the ordering of the regular and seasonal VMA factors. Default is theta(B)*Theta(B). |

Estimation of a sesonal VARMA model

`data` |
The data matrix of the observed k-dimensional time series |

`order` |
The reqular order (p,d,q) |

`sorder` |
The seasonal order (P,D,Q) |

`period` |
Seasonality |

`cnst` |
A logical switch for the constant term |

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

`secoef` |
Standard errors of the parameter estimates |

`residuals` |
Residual series |

`Sigma` |
Residual covariance matrix |

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

`regPhi` |
Regular AR coefficients, if any |

`seaPhi` |
Seasonal AR coefficients |

`regTheta` |
Regular MA coefficients |

`seaTheta` |
Seasonal MA coefficients |

`Ph0` |
The constant vector, if any |

`switch` |
The logical switch to change the ordering of matrix product |

Ruey S. Tsay

Tsay (2014, Chapter 6). Multivariate Time Series Analysis with R and Financial Applications. John Wiley. Hoboken, NJ.

sVARMA

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.