The New Palgrave Dictionary of Economics

Living Edition
| Editors: Palgrave Macmillan

Bayesian Time Series Analysis

  • Mark F. J. Steel
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DOI: https://doi.org/10.1057/978-1-349-95121-5_2737-1
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Abstract

This article describes the use of Bayesian methods in the statistical analysis of time series. The use of Markov chain Monte Carlo methods has made even the more complex time series models amenable to Bayesian analysis. Models discussed in some detail are ARIMA models and their fractionally integrated counterparts, state space models, Markov switching and mixture models, and models allowing for time-varying volatility. A final section reviews some recent approaches to nonparametric Bayesian modelling of time series.

Keywords

ARCH models ARFIMA models ARIMA models ARMA models Bayes factor Bayesian inference Bayesian methods in econometrics Bayesian model averaging Bayesian nonparametrics Bayesian time series analysis Business cycles Cointegration Computational algorithms Conditional likelihood Continuous-time models Convergence clubs Data augmentation Dirichlet processes Forecasting GARCH models Gibbs sampler Growth regressions Hidden Markov models Impulse response function Kalman filter latent states Lévy processes Long-memory models Macroeconomic forecasting Markov chain Monte Carlo methods Markov switching models Metropolis Hastings sampler Nonparametric models Ornstein–Uhlenbeck processes Posterior odds Prediction Prior odds Regime switching models Regression Sequential learning Spatial statistics State space models Stochastic volatility models Survival analysis Threshold autoregressive models Time series analysis Uncertainty Unit roots Vector autoregressions 

JEL Classification

C11 C22 
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© The Author(s) 2008

Authors and Affiliations

  • Mark F. J. Steel
    • 1
  1. 1.