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Simulation-based Bayesian inference for economic time series

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  • John Geweke

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.

Suggested Citation

  • John Geweke, 1996. "Simulation-based Bayesian inference for economic time series," Working Papers 570, Federal Reserve Bank of Minneapolis.
    • Handle: RePEc:fip:fedmwp:570
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    Cited by:

    1. Efthymios G. Tsionas, 2002. "Stochastic frontier models with random coefficients," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(2), pages 127-147.
    2. Tsionas, Efthymios G., 2001. "Euro-land: any good for the European South?," Journal of Policy Modeling, Elsevier, vol. 23(1), pages 67-81, January.
    3. Efthymios Tsionas, 2001. "Regional Convergence and Common, Stochastic Long-run Trends: A Re-examination of the US Regional Data," Regional Studies, Taylor & Francis Journals, vol. 35(8), pages 689-696.
    4. Efthymios Tsionas, 2003. "Inflation and Productivity in Europe: An Empirical Investigation," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 30(1), pages 39-62, March.

    More about this item

    Keywords

    Econometrics;

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