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An Asymtotic Theory of Bayesian Inference for Time Series

Peter C. B. Phillips and Werner Ploberger
Econometrica
Vol. 64, No. 2 (Mar., 1996), pp. 381-412
Published by: The Econometric Society
DOI: 10.2307/2171788
Stable URL: http://www.jstor.org/stable/2171788
Page Count: 32
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An Asymtotic Theory of Bayesian Inference for Time Series
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Abstract

This paper develops an asymptotic theory of Bayesian inference for time series. A limiting representation of the Bayesian data density is obtained and shown to be of the same general exponential form for a wide class of likelihoods and prior distributions. Continuous time and discrete time cases are studied. In discrete time, an embedding theorem is given which shows how to embed the exponential density in a continuous time process. From the embedding we obtain a large sample approximation to the model of the data that corresponds to the exponential density. This has the form of discrete observations drawn from a nonlinear stochastic differential equation driven by Brownian motion. No assumptions concerning stationarity or rates of convergence are required in the asymptotics. Some implications for statistical testing are explored and we suggest tests that are based on likelihood ratios (or Bayes factors) of the exponential densities for discriminating between models.

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