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Akaike-type criteria and the reliability of inference: Model selection versus statistical model specification

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  • Spanos, Aris

Abstract

Since the 1990s, the Akaike Information Criterion (AIC) and its various modifications/extensions, including BIC, have found wide applicability in econometrics as objective procedures that can be used to select parsimonious statistical models. The aim of this paper is to argue that these model selection procedures invariably give rise to unreliable inferences, primarily because their choice within a prespecified family of models (a) assumes away the problem of model validation, and (b) ignores the relevant error probabilities. This paper argues for a return to the original statistical model specification problem, as envisaged by Fisher (1922), where the task is understood as one of selecting a statistical model in such a way as to render the particular data a truly typical realization of the stochastic process specified by the model in question. The key to addressing this problem is to replace trading goodness-of-fit against parsimony with statistical adequacy as the sole criterion for when a fitted model accounts for the regularities in the data.

Suggested Citation

  • Spanos, Aris, 2010. "Akaike-type criteria and the reliability of inference: Model selection versus statistical model specification," Journal of Econometrics, Elsevier, vol. 158(2), pages 204-220, October.
  • Handle: RePEc:eee:econom:v:158:y:2010:i:2:p:204-220
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    References listed on IDEAS

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    1. Bénédicte Vidaillet & V. D'Estaintot & P. Abécassis, 2005. "Introduction," Post-Print hal-00287137, HAL.
    2. Elena Andreou & Aris Spanos, 2003. "Statistical Adequacy and the Testing of Trend Versus Difference Stationarity," Econometric Reviews, Taylor & Francis Journals, vol. 22(3), pages 217-237, January.
    3. Aris Spanos & Anya McGuirk, 2001. "The Model Specification Problem from a Probabilistic Reduction Perspective," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 83(5), pages 1168-1176.
    4. Leeb, Hannes & P tscher, Benedikt M., 2005. "Model Selection And Inference: Facts And Fiction," Econometric Theory, Cambridge University Press, vol. 21(01), pages 21-59, February.
    5. Claeskens,Gerda & Hjort,Nils Lid, 2008. "Model Selection and Model Averaging," Cambridge Books, Cambridge University Press, number 9780521852258, December.
    6. Aris Spanos, 2006. "Revisiting the omitted variables argument: Substantive vs. statistical adequacy," Journal of Economic Methodology, Taylor & Francis Journals, vol. 13(2), pages 179-218.
    7. Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, number 8355, June.
    8. A. Meltzer & Peter Ordeshook & Thomas Romer, 1982. "Introduction," Public Choice, Springer, vol. 39(1), pages 1-3, January.
    9. Spanos, Aris, 1995. "On theory testing in econometrics : Modeling with nonexperimental data," Journal of Econometrics, Elsevier, vol. 67(1), pages 189-226, May.
    10. Aris Spanos, 2001. "Revisiting data mining: 'hunting' with or without a license," Journal of Economic Methodology, Taylor & Francis Journals, vol. 7(2), pages 231-264.
    11. Spanos,Aris, 1986. "Statistical Foundations of Econometric Modelling," Cambridge Books, Cambridge University Press, number 9780521269124, December.
    12. Spanos, Aris, 1990. "The simultaneous-equations model revisited : Statistical adequacy and identification," Journal of Econometrics, Elsevier, vol. 44(1-2), pages 87-105.
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    Cited by:

    1. Anna Conte & Peter Moffatt, 2014. "The econometric modelling of social preferences," Theory and Decision, Springer, vol. 76(1), pages 119-145, January.
    2. repec:gam:jecnmx:v:5:y:2017:i:3:p:38-:d:110889 is not listed on IDEAS
    3. Spanos, Aris, 2010. "Statistical adequacy and the trustworthiness of empirical evidence: Statistical vs. substantive information," Economic Modelling, Elsevier, vol. 27(6), pages 1436-1452, November.

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