Find the best model from a range of possible ARMA models

Estimate AR coefficients using the Burg method

1 | ```
burg(x, p)
``` |

`x` |
Data vector (typically residuals from |

`p` |
AR order |

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` |
0 |

`sigma2` |
White noise variance |

`aicc` |
Akaike information criterion corrected |

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

`se.theta` |
0 |

1 2 3 4 |

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