Bayesian Consistency for Long Memory Processes: A Semiparametric Perspective

TL;DR

This paper develops a Bayesian semiparametric framework for estimating long memory process parameters, addressing challenges in existing methods and establishing asymptotic properties for improved inference in fields like finance and environmental sciences.

Contribution

It introduces a novel Bayesian semiparametric approach for long memory models and analyzes its asymptotic properties, enhancing estimation techniques for complex time series.

Findings

Proposes a new Bayesian semiparametric method for long memory estimation

Establishes asymptotic properties of the proposed model

Enables efficient implementation of nonparametric Bayesian algorithms

Abstract

In this work, we will investigate a Bayesian approach to estimating the parameters of long memory models. Long memory, characterized by the phenomenon of hyperbolic autocorrelation decay in time series, has garnered significant attention. This is because, in many situations, the assumption of short memory, such as the Markovianity assumption, can be deemed too restrictive. Applications for long memory models can be readily found in fields such as astronomy, finance, and environmental sciences. However, current parametric and semiparametric approaches to modeling long memory present challenges, particularly in the estimation process. In this study, we will introduce various methods applied to this problem from a Bayesian perspective, along with a novel semiparametric approach for deriving the posterior distribution of the long memory parameter. Additionally, we will establish the asymptotic properties of the model. An advantage of this approach is that it allows to implement state-of-the-art efficient algorithms for nonparametric Bayesian models.