Diagnostics cannot have much power against general alternatives
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.
References listed on IDEAS
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- Wojciech Olszewski & Alvaro Sandroni, 2008.
"Manipulability of Future-Independent Tests,"
Econometric Society, vol. 76(6), pages 1437-1466, November.
- 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.
- Freedman, David A., 2008. "Survival Analysis: A Primer," The American Statistician, American Statistical Association, vol. 62, pages 110-119, May.
- Peter Kennedy, 2003. "A Guide to Econometrics, 5th Edition," MIT Press Books, The MIT Press, edition 5, volume 1, number 026261183x, July. Full references (including those not matched with items on IDEAS)
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