likelihood_ratio.par: Computes log likelihood ratio for partial autoregressive...

In partialAR: Partial Autoregression

Description

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.

Usage

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

Arguments

X	The numeric vector or zoo vector to which the partially autoregressive model is being fit.
robust	If TRUE, then errors are assumed to follow a t-distribution with nu degrees of freedom. If FALSE, then errors are assumed to follow a normal distribution. Default: FALSE
null_model	Specifies the null hypothesis: "rw" Pure random walk (e.g., sigma_M = 0) "ar1" Pure autoregressive (e.g., sigma_R = 0) Default: "rw"
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: "css"
nu	If robust is TRUE, this specifies the number of degrees of freedom of the t-distribution. Default: 5

Value

A numeric value representing the log likelihood ratio

Author(s)

Matthew Clegg [e-mail]

References

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

See Also

fit.par

partialAR documentation built on April 14, 2020, 6:05 p.m.