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The Large Sample Correspondence between Classical Hypothesis Tests and Bayesian Posterior Odds Tests


  • Andrews, Donald W K


This paper establishes a correspondence in large samples between classical hypothesis tests and Bayesian posterior odds tests for models without trends. More specifically, tests of point null hypotheses and one- or two-sided alternatives are considered (where nuisance parameters may be present under both hypotheses). It is shown that, for certain priors, the Bayesian posterior odds test is equivalent in large samples to classical Wald, Lagrange multiplier, and likelihood ratio tests for some significance level and vice versa. The priors considered under the alternative hypothesis are taken to shrink to the null hypothesis at rate n[superscript -1/2] as the sample size n increases. Copyright 1994 by The Econometric Society.

Suggested Citation

  • Andrews, Donald W K, 1994. "The Large Sample Correspondence between Classical Hypothesis Tests and Bayesian Posterior Odds Tests," Econometrica, Econometric Society, vol. 62(5), pages 1207-1232, September.
  • Handle: RePEc:ecm:emetrp:v:62:y:1994:i:5:p:1207-32

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    References listed on IDEAS

    1. Nelson, Forrest D & Savin, N E, 1990. "The Danger of Extrapolating Asymptotic Local Power," Econometrica, Econometric Society, vol. 58(4), pages 977-981, July.
    2. Killingsworth, Mark R. & Heckman, James J., 1987. "Female labor supply: A survey," Handbook of Labor Economics,in: O. Ashenfelter & R. Layard (ed.), Handbook of Labor Economics, edition 1, volume 1, chapter 2, pages 103-204 Elsevier.
    3. Amemiya, Takeshi, 1981. "Qualitative Response Models: A Survey," Journal of Economic Literature, American Economic Association, vol. 19(4), pages 1483-1536, December.
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    Cited by:

    1. Ghysels, Eric & Guay, Alain, 2003. "Structural change tests for simulated method of moments," Journal of Econometrics, Elsevier, vol. 115(1), pages 91-123, July.
    2. Götz, Thomas B. & Hecq, Alain & Smeekes, Stephan, 2016. "Testing for Granger causality in large mixed-frequency VARs," Journal of Econometrics, Elsevier, vol. 193(2), pages 418-432.
    3. Hoshino, Takahiro, 2008. "Bayesian significance testing and multiple comparisons from MCMC outputs," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3543-3559, March.
    4. Eric Ghysels & Alain Guay, 2001. "Testing for Structural Change in the Presence of Auxiliary Models," Cahiers de recherche CREFE / CREFE Working Papers 133, CREFE, Université du Québec à Montréal.
    5. Berg, Nathan, 2004. "No-decision classification: an alternative to testing for statistical significance," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 33(5), pages 631-650, November.
    6. Chernozhukov, Victor & Hong, Han, 2003. "An MCMC approach to classical estimation," Journal of Econometrics, Elsevier, vol. 115(2), pages 293-346, August.
    7. Penelope Smith, 2006. "Bayesian Inference for a Threshold Autoregression with a Unit Root," Melbourne Institute Working Paper Series wp2006n20, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    8. Lavergne, Pascal, 2014. "Model equivalence tests in a parametric framework," Journal of Econometrics, Elsevier, vol. 178(P3), pages 414-425.

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