Find the best model from a range of possible ARMA models

1 |

`x` |
Data vector (typically residuals from |

`p` |
Range of AR orders |

`q` |
Range of MA orders |

Tries all combinations of `p`

and `q`

and returns the
model with the lowest AICC.
The arguments `p`

and `q`

should be small ranges as this function
can be slow otherwise.
The innovations algorithm is used to estimate white noise variance.

Returns an ARMA model consisting of a list with the following components.

`phi` |
Vector of AR coefficients (index number equals coefficient subscript) |

`theta` |
Vector of MA coefficients (index number equals coefficient subscript) |

`sigma2` |
White noise variance |

`aicc` |
Akaike information criterion corrected |

`se.phi` |
Standard errors for the AR coefficients |

`se.theta` |
Standard errors for the MA coefficients |

1 2 3 4 |

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