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Gibbs Sampling in AR Models with Random Walk Priors

  • Wolfgang Polasek
  • Song Jin
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Summary

The paper analyses univariate autoregressive AR(p) models with tightness prior. The framework of the model is the conjugate normal linear model where the prior distribution is assumed to be a random walk process. The deviation from the prior distribution is measured by the tightness (hyper-) parameter λ. It is shown how the estimation of the starting values can be incorporated into the Gibbs sampling scheme. We demonstrate this approach with simulated and economic time series. It is found that for typical economic sample size the sampling fluctuations influence the posterior distribution considerably and informative prior distributions seem to be useful, especially for prediction.

Keywords

Posterior Distribution Prior Distribution Gibbs Sampler Random Walk Process Full Conditional Distribution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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Copyright information

© Springer-Verlag Berlin · Heidelberg 1996

Authors and Affiliations

  • Wolfgang Polasek
    • 1
  • Song Jin
    • 1
  1. 1.Institute for Statistics and EconometricsUniversity of BaselBaselSwitzerland

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