Performs Granger causality test using a vector autoregressive model

1 | ```
GrangerTest(X,p=1,include.mean=T,locInput=c(1))
``` |

`X` |
a T-by-p data matrix with T denoting sample size and p the number of variables |

`p` |
vector AR order. |

`include.mean` |
Indicator for including a constant in the model. Default is TRUE. |

`locInput` |
Locators for the input variables in the data matrix. Deafult is the first column being the input variable. Multiple inputs are allowed. |

Perform VAR(p) and constrained VAR(p) estimations to test the Granger causality. It uses likelihood ratio and asymptotic chi-square.

`data` |
Original data matrix |

`cnst` |
logical variable to include a constant in the model |

`order` |
order of VAR model used |

`coef` |
Coefficient estimates |

`constraints` |
Implied constraints of Granger causality |

`aic, bic, hq` |
values of information criteria |

`residuals` |
residual vector |

`secoef` |
standard errors of coefficient estimates |

`Sigma` |
Residual covariance matrix |

`Phi` |
Matrix of VAR coefficients |

`Ph0` |
constant vector |

`omega` |
Estimates of constrained coefficients |

`covomega` |
covariance matrix of constrained parameters |

`locInput` |
Locator vector for input variables |

Ruey S. Tsay

Tsay (2014, Chapter 2)

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