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Bayesian Unit Root Test in Double Threshold Heteroskedastic Models

Listed author(s):
  • Cathy Chen

    ()

  • Shu-Yu Chen
  • Sangyeol Lee

This paper aims to detect the presence of local non-stationarity of nonlinear autoregressive processes with heteroskedastic errors. A Bayesian test is developed to test for the unit root in multi-regime threshold autoregression with heteroskedasticity. To implement a test, a posterior odds analysis is proposed. Particularly, a mixture prior for the autoregressive coefficient is used to alleviate the identifiability problem that occurs when time series has unit roots. The proposed method achieves a reliable inference despite of the non-integrability problem in the likelihood function. A simulation study and two real data analysis are conducted for illustration. This paper successfully proves the proposed model can accommodate different threshold values to cope with local non-stationarity and in addition, captures discrete time-varying properties. Copyright Springer Science+Business Media New York 2013

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File URL: http://hdl.handle.net/10.1007/s10614-012-9354-7
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Article provided by Springer & Society for Computational Economics in its journal Computational Economics.

Volume (Year): 42 (2013)
Issue (Month): 4 (December)
Pages: 471-490

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Handle: RePEc:kap:compec:v:42:y:2013:i:4:p:471-490
DOI: 10.1007/s10614-012-9354-7
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