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Bayesian Analysis of Deterministic Time Trend and Changes in Persistence Using a Generalised Stochastic Unit Root Model

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  • Fuyu Yang

Abstract

This paper makes use of the novel Generalized Stochastic Unit Root (GSTUR) model, Bayesian model estimation and model comparison techniques to investigate the presence of a deterministic time trend in economic series. The model is specified to allow for changes in persistence over time, such as shifts from stationarity I(0) to nonstationarity I(1) or vice versa. This uncertainty raises the crucial question about how sure one can be that an economic time series has a deterministic trend when there is a change in the underlying properties. Empirical analysis indicates that the GSTUR model could provide new insights on time series studies.

Suggested Citation

  • Fuyu Yang, 2007. "Bayesian Analysis of Deterministic Time Trend and Changes in Persistence Using a Generalised Stochastic Unit Root Model," Discussion Papers in Economics 07/11, Division of Economics, School of Business, University of Leicester.
    • Handle: RePEc:lec:leecon:07/11
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    File URL: https://www.le.ac.uk/economics/research/RePEc/lec/leecon/dp07-11.pdf
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    References listed on IDEAS

    as
    1. Han C. & Carlin B. P., 2001. "Markov Chain Monte Carlo Methods for Computing Bayes Factors: A Comparative Review," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1122-1132, September.
    2. Kapetanios, George & Shin, Yongcheol & Snell, Andy, 2003. "Testing for a unit root in the nonlinear STAR framework," Journal of Econometrics, Elsevier, vol. 112(2), pages 359-379, February.
    3. George Kapetanios & Yongcheol Shin & Andy Snell, 2000. "Testing for a Unit Root against Nonlinear STAR Models," Edinburgh School of Economics Discussion Paper Series 69, Edinburgh School of Economics, University of Edinburgh.
    4. Koop, Gary & Ley, Eduardo & Osiewalski, Jacek & Steel, Mark F. J., 1997. "Bayesian analysis of long memory and persistence using ARFIMA models," Journal of Econometrics, Elsevier, vol. 76(1-2), pages 149-169.
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