Perform estimation of a VARMA model specified via the SCM approach

1 2 3 |

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

`scms` |
A k-by-2 matrix of the orders of SCMs |

`Tdx` |
A k-dimensional vector for locating "1" of each row in the transformation matrix. |

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

`fixed` |
A logical matrix to set parameters to zero |

`prelim` |
A logical switch for preliminary estimation. Default is false. |

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

`thres` |
Threshold for individual t-ratio when setting parameters to zero. Default is 1. |

`ref` |
A switch to use SCMmod in model specification. |

`SCMpar` |
Parameter estimates of the SCM model, to be used in model refinement |

`seSCMpar` |
Standard errors of the parameter estimates in SCMpar |

Perform conditional maximum likelihood estimation of a VARMA model specified by the scalar component model approach, including the transformation matrix.

`data` |
Observed time series |

`SCMs` |
The specified SCMs |

`Tdx` |
Indicator vector for the transformation matrix. The length of Tdx is k. |

`locTmtx` |
Specification of estimable parameters of the transformation matrix |

`locAR` |
Locators for the estimable parameters of the VAR coefficients |

`locMA` |
Locators for the estimable parameters of the VMA coefficients |

`cnst` |
A logical switch to include the constant vector in the model |

`coef` |
The parameter estimates |

`secoef` |
Standard errors of the parameter estimates |

`residuals` |
Residual series |

`Sigma` |
Residual covariance matrix |

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

`Ph0` |
Estimates of the constant vector, if any |

`Phi` |
Estimates of the VAR coefficients |

`Theta` |
Estimates of the VMA coefficients |

Ruey S. Tsay

Tsay (2014, Chapter 4). Multivariate Time Series Analysis with R and Financial Applications. John Wiley. Hoboken, NJ.

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