Simulation-based Bayesian inference for economic time series

John Geweke

No 570, Working Papers from Federal Reserve Bank of Minneapolis

Abstract: This paper surveys recently developed methods for Bayesian inference and their use in economic time series models. It begins by reviewing aspects of Bayesian inference essential to understanding the implications of the Bayesian paradigm for time series analysis. It next describes the use of posterior simulators to solve otherwise intractable analytical problems. The theory and the computational advances are brought together in setting forth a practical framework for decision-making and forecasting. These developments are illustrated in the context of the vector autoregressions, stochastic volatility models, and models of changing regimes.

Keywords: Econometrics (search for similar items in EconPapers)
Date: 1996
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Citations: View citations in EconPapers (4)

Published in Simulation- based inference in econometrics (2000, pp. 255-299)

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http://www.minneapolisfed.org/research/WP/WP570.pdf

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Persistent link: https://EconPapers.repec.org/RePEc:fip:fedmwp:570

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