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A Bayesian analysis of generalized threshold autoregressive models

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  • Chen, Cathy W. S.

Abstract

The threshold autoregressive (TAR) model is generalized which results in more flexibility in applications. We construct a Bayesian framework to show that Markov chain Monte Carlo method can be applied to estimating parameters with success.

Suggested Citation

  • Chen, Cathy W. S., 1998. "A Bayesian analysis of generalized threshold autoregressive models," Statistics & Probability Letters, Elsevier, vol. 40(1), pages 15-22, September.
    • Handle: RePEc:eee:stapro:v:40:y:1998:i:1:p:15-22
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    1. Deutsch, Melinda & Granger, Clive W. J. & Terasvirta, Timo, 1994. "The combination of forecasts using changing weights," International Journal of Forecasting, Elsevier, vol. 10(1), pages 47-57, June.
    2. Cathy W. S. Chen & Jack C. Lee, 1995. "Bayesian Inference Of Threshold Autoregressive Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 16(5), pages 483-492, September.
    3. Nicholas G. Polson & George C. Tiao (ed.), 1995. "Bayesian Inference," Books, Edward Elgar Publishing, volume 0, number 602.
    4. John Geweke & Nobuhiko Terui, 1993. "Bayesian Threshold Autoregressive Models For Nonlinear Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 14(5), pages 441-454, September.
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    4. Andrea Carolina Vargas-Páez & Carlos David Ardila-Dueñas, 2021. "Efecto del riesgo de tipo de cambio en la rentabilidad de los bonos soberanos en Colombia," Borradores de Economia 1165, Banco de la Republica de Colombia.
    5. Vugar Ahmadov & Shaig Adigozalov & Salman Huseynov & Fuad Mammadov & Vugar Rahimov, 2016. "Forecasting inflation in post-oil boom years: A case for non-linear models?," Working Papers 1601, Central Bank of Azerbaijan Republic.
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    10. Carlo Pizzinelli & Konstantinos Theodoridis & Francesco Zanetti, 2020. "State Dependence In Labor Market Fluctuations," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 61(3), pages 1027-1072, August.
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