Bayesian Lattice Filters for Time-Varying Autoregression and Time-Frequency Analysis

TL;DR

This paper introduces a Bayesian lattice filter method for modeling nonstationary time series using time-varying autoregressive models, providing accurate, computationally efficient time-frequency analysis across diverse scientific fields.

Contribution

It presents a novel Bayesian lattice filter approach in the partial autocorrelation domain for time-varying autoregressive modeling, improving estimation stability and efficiency.

Findings

Outperforms competing methods in spectral density estimation accuracy

Demonstrates effectiveness on ecological, environmental, and economic data

Provides stable, fast computation without matrix inversions

Abstract

Modeling nonstationary processes is of paramount importance to many scientific disciplines including environmental science, ecology, and finance, among others. Consequently, flexible methodology that provides accurate estimation across a wide range of processes is a subject of ongoing interest. We propose a novel approach to model-based time-frequency estimation using time-varying autoregressive models. In this context, we take a fully Bayesian approach and allow both the autoregressive coefficients and innovation variance to vary over time. Importantly, our estimation method uses the lattice filter and is cast within the partial autocorrelation domain. The marginal posterior distributions are of standard form and, as a convenient by-product of our estimation method, our approach avoids undesirable matrix inversions. As such, estimation is extremely computationally efficient and stable. To illustrate the effectiveness of our approach, we conduct a comprehensive simulation study that compares our method with other competing methods and find that, in most cases, our approach performs superior in terms of average squared error between the estimated and true time-varying spectral density. Lastly, we demonstrate our methodology through three modeling applications; namely, insect communication signals, environmental data (wind components), and macroeconomic data (US gross domestic product (GDP) and consumption).