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Bayesian time series analysis

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Macroeconometrics and Time Series Analysis

Part of the book series: The New Palgrave Economics Collection ((NPHE))

Abstract

The importance of Bayesian methods in econometrics has increased rapidly since the early 1990s. This has, no doubt, been fuelled by an increasing appreciation of the advantages that Bayesian inference entails. In particular, it provides us with a formal way to incorporate the prior information we often possess before seeing the data, it fits perfectly with sequential learning and decision making, and it directly leads to exact small sample results. In addition, the Bayesian paradigm is particularly natural for prediction, since we take into account all parameter or even model uncertainty. The predictive distribution is the sampling distribution where the parameters are integrated out with the posterior distribution and provides exactly what we need for forecasting, often a key goal of time-series analysis.

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Authors
  1. Mark F. J. Steel
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Editor information

Editors and Affiliations

  1. University of Wisconsin-Madison, USA

    Steven N. Durlauf

  2. Cornell University, USA

    Lawrence E. Blume

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© 2010 Palgrave Macmillan, a division of Macmillan Publishers Limited

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Steel, M.F.J. (2010). Bayesian time series analysis. In: Durlauf, S.N., Blume, L.E. (eds) Macroeconometrics and Time Series Analysis. The New Palgrave Economics Collection. Palgrave Macmillan, London. https://doi.org/10.1057/9780230280830_4

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