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Phase randomisation: a convergence diagnostic test for MCMC

Author

Listed:
  • Rodney C Wolff
  • Darfiana Nur
  • Kerrie L Mengersen

    (School of Economics and Finance, Queensland University of Technology)

Abstract

Most MCMC users address the convergence problem by applying diagnostic tools to the output produced by running their samplers. Potentially useful diagnostics may be borrowed from diverse areas such as time series. One such method is phase randomisation. The aim of this paper is to describe this method in the context of MCMC, summarise its characteristics, and contrast its performance with those of the more common diagnostic tests for MCMC. It is observed that the new tool contributes information about third and higher order cumulant behaviour which is important in characterising certain forms of nonlinearity and nonstationarity.

Suggested Citation

  • Rodney C Wolff & Darfiana Nur & Kerrie L Mengersen, 2006. "Phase randomisation: a convergence diagnostic test for MCMC," School of Economics and Finance Discussion Papers and Working Papers Series 208e, School of Economics and Finance, Queensland University of Technology.
  • Handle: RePEc:qut:dpaper:208e
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    File URL: http://eprints.qut.edu.au/5932/
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    References listed on IDEAS

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    5. Yao, Qiwei & Tong, Howell, 1994. "Quantifying the influence of initial values on nonlinear prediction," LSE Research Online Documents on Economics 19426, London School of Economics and Political Science, LSE Library.
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    8. Newey, Whitney K & Powell, James L, 1987. "Asymmetric Least Squares Estimation and Testing," Econometrica, Econometric Society, vol. 55(4), pages 819-847, July.
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