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Bayesian Inference for a Threshold Autoregression with a Unit Root

  • Penelope Smith

    (Melbourne Institute of Applied Economic and Social Research, The University of Melbourne)

Registered author(s):

    A Bayesian approach to distinguishing between nonlinear and unit root behavior offers several practical advantages over equivalent frequentist procedures. Foremost among these advantages is the simplicity of the test. This paper compares the small sample power and size properties of a joint Bayesian test for a unit root and a threshold effect with Caner and Hansen's (2001) frequentist strategy. The results from Monte Carlo experiments indicate that the simpler Bayesian test performs at least as well as Caner and Hansen's procedure.

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    File URL: http://www.melbourneinstitute.com/downloads/working_paper_series/wp2006n20.pdf
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    Paper provided by Melbourne Institute of Applied Economic and Social Research, The University of Melbourne in its series Melbourne Institute Working Paper Series with number wp2006n20.

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    Length: 40 pages
    Date of creation: Oct 2006
    Date of revision:
    Handle: RePEc:iae:iaewps:wp2006n20
    Contact details of provider: Postal: Melbourne Institute of Applied Economic and Social Research, The University of Melbourne, Victoria 3010 Australia
    Phone: +61 3 8344 2100
    Fax: +61 3 8344 2111
    Web page: http://www.melbourneinstitute.com/
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    1. Christopher A. Sims, 1988. "Bayesian skepticism on unit root econometrics," Discussion Paper / Institute for Empirical Macroeconomics 3, Federal Reserve Bank of Minneapolis.
    2. Hansen, Bruce E, 1996. "Inference When a Nuisance Parameter Is Not Identified under the Null Hypothesis," Econometrica, Econometric Society, vol. 64(2), pages 413-30, March.
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    6. Frédérique BEC & Mélika BEN SALEM & Ronald MACDONALD, 2006. "Real exchange rates and real interest rates : a nonlinear perspective," Discussion Papers (REL - Recherches Economiques de Louvain) 2006024, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
    7. Philip Arestis & Andrea Cipollini & Bassam Fattouh, 2004. "Threshold Effects in the U.S. Budget Deficit," Economic Inquiry, Western Economic Association International, vol. 42(2), pages 214-222, April.
    8. Marriott, John & Newbold, Paul, 2000. "The strength of evidence for unit autoregressive roots and structural breaks: A Bayesian perspective," Journal of Econometrics, Elsevier, vol. 98(1), pages 1-25, September.
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    10. Elliott, Graham & Rothenberg, Thomas J & Stock, James H, 1996. "Efficient Tests for an Autoregressive Unit Root," Econometrica, Econometric Society, vol. 64(4), pages 813-36, July.
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    18. Eric Zivot & Peter C.B. Phillips, 1991. "A Bayesian Analysis of Trend Determination in Economic Time Series," Cowles Foundation Discussion Papers 1002, Cowles Foundation for Research in Economics, Yale University.
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    20. Robert B. Davies, 2002. "Hypothesis testing when a nuisance parameter is present only under the alternative: Linear model case," Biometrika, Biometrika Trust, vol. 89(2), pages 484-489, June.
    21. DeJong, David N. & Whiteman, Charles H., 1991. "Reconsidering 'trends and random walks in macroeconomic time series'," Journal of Monetary Economics, Elsevier, vol. 28(2), pages 221-254, October.
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    23. Kim, Jae-Young, 1994. "Bayesian Asymptotic Theory in a Time Series Model with a Possible Nonstationary Process," Econometric Theory, Cambridge University Press, vol. 10(3-4), pages 764-773, August.
    24. Koop, Gary & Potter, Simon M., 1998. "Bayes factors and nonlinearity: Evidence from economic time series1," Journal of Econometrics, Elsevier, vol. 88(2), pages 251-281, November.
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