Description Usage Arguments Value Author(s) References See Also

Computes the log likelihood ratio for the partially autoregressive model.

First, a fit is performed for the specified null model. Then, a fit is performed for the alternative model that the sequence is partially autoregressive. The likelihood scores are computed for both models, and the log likelihood ratio is returned.

1 2 | ```
likelihood_ratio.par(X, robust = FALSE, null_model = c("rw", "ar1"),
opt_method = c("css", "kfas", "ss"), nu = par.nu.default())
``` |

`X` |
The numeric vector or zoo vector to which the partially autoregressive model is being fit. |

`robust` |
If |

`null_model` |
Specifies the null hypothesis: "rw" Pure random walk (e.g., `sigma_M = 0` )"ar1" Pure autoregressive (e.g., `sigma_R = 0` )
Default: |

`opt_method` |
The method to be used for calculating the negative log likelihood. -
`"ss"` Steady-state Kalman filter with normally distributed errors -
`"css"` Steady-state Kalman filter with normally distributed errors, coded in C++ -
`"kfas"` Traditional Kalman filter of the`KFAS` package
Default: |

`nu` |
If |

A numeric value representing the log likelihood ratio

Matthew Clegg [email protected]

Clegg, Matthew.
Modeling Time Series with Both Permanent and Transient Components
using the Partially Autoregressive Model.
*Available at SSRN: http://ssrn.com/abstract=2556957*

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