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Diagnostics cannot have much power against general alternatives

  • Freedman, David A.
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    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.

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    File URL: http://www.sciencedirect.com/science/article/B6V92-4WC4WGV-1/2/65b455fef7f3b1294ed44c41f744e42c
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    Article provided by Elsevier in its journal International Journal of Forecasting.

    Volume (Year): 25 (2009)
    Issue (Month): 4 (October)
    Pages: 833-839

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    Handle: RePEc:eee:intfor:v:25:y:2009:i:4:p:833-839
    Contact details of provider: Web page: http://www.elsevier.com/locate/ijforecast

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    1. Freedman, David A., 2008. "Survival Analysis: A Primer," The American Statistician, American Statistical Association, vol. 62, pages 110-119, May.
    2. Alvaro Sandroni & Wojciech Olszewski, 2008. "Manipulability of Future-Independent Tests," PIER Working Paper Archive 08-014, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    3. Peter Kennedy, 2003. "A Guide to Econometrics, 5th Edition," MIT Press Books, The MIT Press, edition 5, volume 1, number 026261183x, June.
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