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How Can We Get Rid Of Dogmatic Prior Information?

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  • Kloek, T.

Abstract

In this paper it is argued that conclusions cannot be sturdy if they are based upon unchecked dogmatic prior information. The vehicle chosen to evaluate models is their out-of-sample prediction performance. If model M predicts systematically better than model N we should stop using N, but if the difference in predictive quality is mainly caused by a few very influential observations there is reason for serious doubt. The testing point of view of McAleer et al. (1985) and many others is adopted, but it is demonstrated that some of the tests may be misleading. The author agrees with the conclusion of Learner (1985) that sensitivity analysis is important but he prefers different tools of analysis and a different reporting style.

Suggested Citation

  • Kloek, T., 1986. "How Can We Get Rid Of Dogmatic Prior Information?," Econometric Institute Archives 272348, Erasmus University Rotterdam.
    • Handle: RePEc:ags:eureia:272348
    DOI: 10.22004/ag.econ.272348
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    References listed on IDEAS

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    4. Krasker, William S. & Kuh, Edwin & Welsch, Roy E., 1983. "Estimation for dirty data and flawed models," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 1, chapter 11, pages 651-698, Elsevier.
    5. Amemiya, Takeshi, 1980. "Selection of Regressors," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 21(2), pages 331-354, June.
    6. McManus, Walter S, 1985. "Estimates of the Deterrent Effect of Capital Punishment: The Importance of the Researcher's Prior Beliefs," Journal of Political Economy, University of Chicago Press, vol. 93(2), pages 417-425, April.
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