ar_lik

mean

logical; if TRUE, probabilities p are given as log(p)

logical; if TRUE, the full likelihood for all observations is computed; if FALSE, the partial likelihood for the last n-p observations

full

Likelihood of an autoregressive time series model with i.i.d. normal innovations

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

ar_lik: Likelihood of an autoregressive time series model with i.i.d. normal innovations

Description

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