psd_dummy_model

<p>Time series model X_t=e_t, E[e_t]=0, without additional parameters
Not public, since encapsulated in an extra function, for easier usage</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

psd_dummy_model function

Time series model X_t=e_t, E[e_t]=0, without additional parameters
Not public, since encapsulated in an extra function, for easier usage — psd_dummy_model

Time series model X_t=e_t, E[e_t]=0, without additional parameters
Not public, since encapsulated in an extra function, for easier usage

psd_dummy_model: Time series model X_t=e_t, E[e_t]=0, without additional parameters Not public, since encapsulated in an extra function, for easier usage

Description

Usage

Arguments