Open Access
December 2016 Bayesian Lattice Filters for Time-Varying Autoregression and Time–Frequency Analysis
Wen-Hsi Yang, Scott H. Holan, Christopher K. Wikle
Bayesian Anal. 11(4): 977-1003 (December 2016). DOI: 10.1214/15-BA978


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).


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Wen-Hsi Yang. Scott H. Holan. Christopher K. Wikle. "Bayesian Lattice Filters for Time-Varying Autoregression and Time–Frequency Analysis." Bayesian Anal. 11 (4) 977 - 1003, December 2016.


Published: December 2016
First available in Project Euclid: 19 October 2015

zbMATH: 1359.62390
MathSciNet: MR3545471
Digital Object Identifier: 10.1214/15-BA978

Keywords: Locally stationary , Model selection , nonstationary , partial autocorrelation , piecewise stationary , sequential estimation , time-varying spectral density

Vol.11 • No. 4 • December 2016
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