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Diagnostics cannot have much power against general alternatives

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  • Freedman, David A.

Abstract

Model diagnostics are shown to have little power unless alternative hypotheses can be narrowly defined. For example, the independence of observations cannot be tested against general forms of dependence. Thus, the basic assumptions in regression models cannot be inferred from the data. Equally, the proportionality assumption in proportional-hazards models is not testable. Specification error is a primary source of uncertainty in forecasting, and this uncertainty will be difficult to resolve without external calibration. Model-based causal inference is even more problematic.

Suggested Citation

  • Freedman, David A., 2009. "Diagnostics cannot have much power against general alternatives," International Journal of Forecasting, Elsevier, vol. 25(4), pages 833-839, October.
  • Handle: RePEc:eee:intfor:v:25:y:2009:i:4:p:833-839
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    References listed on IDEAS

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    1. Wojciech Olszewski & Alvaro Sandroni, 2008. "Manipulability of Future-Independent Tests," Econometrica, Econometric Society, vol. 76(6), pages 1437-1466, November.
    2. Freedman, David A., 2008. "Survival Analysis: A Primer," The American Statistician, American Statistical Association, vol. 62, pages 110-119, May.
    3. Peter Kennedy, 2003. "A Guide to Econometrics, 5th Edition," MIT Press Books, The MIT Press, edition 5, volume 1, number 026261183x, January.
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    Cited by:

    1. Zhang, Shulin & Okhrin, Ostap & Zhou, Qian M. & Song, Peter X.-K., 2016. "Goodness-of-fit test for specification of semiparametric copula dependence models," Journal of Econometrics, Elsevier, vol. 193(1), pages 215-233.
    2. Shulin Zhang, & Ostap Okhrin, & Qian M. Zhou & Peter X.-K. Song, 2013. "Goodness-of-fit Test for Specification of Semiparametric Copula Dependence Models," SFB 649 Discussion Papers SFB649DP2013-041, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.

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