arma_conditional

<p>Function for computing the ARMA(p, q) conditional likelihood
Models with AR component: zt are from t = p + 1 to m
Models with MA component: eps_0 = ... = eps_q+1 = 0
The input zt should be FCFZ, i.e., data corrected in freq. domain then IFTd
FCFZ should be FCFZ[-c(1, n)]; i.e., have removed elements 1 and n beforehand
No sigma2 in here anymore!
...: Further arguments to be passed to likelihood function</p>

internal

Implementations of a Bayesian parametric (autoregressive), a Bayesian nonparametric (Whittle likelihood with Bernstein-Dirichlet prior) and a Bayesian semiparametric (autoregressive likelihood with Bernstein-Dirichlet correction) procedure are provided. The work is based on the corrected parametric likelihood by C. Kirch et al (2017) <arXiv:1701.04846>. It was supported by DFG grant KI 1443/3-1.

Alexander Meier

beyondWhittle

Bayesian Spectral Inference for Stationary Time Series

Claudia Kirch

Matthew C. Edwards

Renate Meyer

arma_conditional function

Function for computing the ARMA(p, q) conditional likelihood
Models with AR component: zt are from t = p + 1 to m
Models with MA component: eps_0 = ... = eps_q+1 = 0
The input zt should be FCFZ, i.e., data corrected in freq. domain then IFTd
FCFZ should be FCFZ[-c(1, n)]; i.e., have removed elements 1 and n beforehand
No sigma2 in here anymore!
...: Further arguments to be passed to likelihood function — arma_conditional

Function for computing the ARMA(p, q) conditional likelihood
Models with AR component: zt are from t = p + 1 to m
Models with MA component: eps_0 = ... = eps_q+1 = 0
The input zt should be FCFZ, i.e., data corrected in freq. domain then IFTd
FCFZ should be FCFZ[-c(1, n)]; i.e., have removed elements 1 and n beforehand
No sigma2 in here anymore!
...: Further arguments to be passed to likelihood function

Description

Usage

Arguments