Posterior consistency for the spectral density of non-Gaussian stationary time series

Abstract

Various nonparametric approaches for Bayesian spectral density estimation of stationary time series have been suggested in the literature, mostly based on the Whittle likelihood approximation. A generalization of this approximation has been proposed in Kirch et al. who prove posterior consistency for spectral density estimation in combination with the Bernstein-Dirichlet process prior for Gaussian time series. In this paper, we will extend the posterior consistency result to non-Gaussian time series by employing a general consistency theorem of Shalizi for dependent data and misspecified models. As a special case, posterior consistency for the spectral density under the Whittle likelihood as proposed by Choudhuri, Ghosal and Roy is also extended to non-Gaussian time series. Small sample properties of this approach are illustrated with several examples of non-Gaussian time series.

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• Basic Properties of Strong Mixing Conditions. A Survey and Some Open Questions
• Misspecification in infinite-dimensional Bayesian statistics
• Central limit theorem for Fourier transforms of stationary processes
• Bayesian nonparametric spectral density estimation using B-spline priors
• Reconciling the Gaussian and Whittle Likelihood with an application to estimation in the frequency domain