Bayesian semiparametric inference on long-range dependence

• 成果类型: Article
• 署名作者: Liseo, B; Marinucci, D; Petrella, L
• 署名单位: Sapienza University Rome
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/88.4.1089
• 发表日期: 2001
• 页码: 10891104
• 关键词: LOG-PERIODOGRAM REGRESSION; time-series; memory; MODEL

摘要

We develop a Bayesian semiparametric procedure for the analysis of stationary long-range dependent time series, We use frequency domain methods to partition the infinite-dimensional parameter space into regions where genuine prior information on the form of the spectral density is available, and others where vague prior beliefs are adopted; the solution to the partition problem, which is equivalent to bandwidth choice from a frequentist point of view, is obtained via Bayes factors. We derive a tight characterisation of the class of admissible noninformative priors for nonparametric inference on the spectral density of a stationary process. Asymptotic properties of our technique and comparisons with frequentist approaches are also considered; the suggested procedure is finally implemented via Markov chain Monte Carlo methods on simulated and real data.