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Statistical adequacy and the trustworthiness of empirical evidence: Statistical vs. substantive information

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  • Spanos, Aris

Abstract

The paper focuses on how the traditional textbook approach to econometrics, by conflating statistical and substantive information, has contributed significantly to the mountains of untrustworthy evidence accumulated over the last century. In a nutshell, the problem is that when one's favorite theory is foisted on the data, the end result is invariably an empirical model which is both statistically and substantively misspecified, but one has no way to disentangle the two sources of error in order to draw reliable inferences. It is argued that ignoring statistical misspecification, and focusing exclusively on the evaluation of the statistical results - taken at face value - on substantive grounds, has proved a disastrous strategy for learning from data. Moreover, the traditional textbook stratagems of error-fixing designed to alleviate statistical misspecification often make matters worse. Instead, the paper proposes a number of strategies that separate the statistical and substantive sources of information, ab initio, and address the problem by replacing goodness-of-fit with statistical adequacy to secure the statistical reliability of inference, and then proceed to pose questions of substantive adequacy.

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Bibliographic Info

Article provided by Elsevier in its journal Economic Modelling.

Volume (Year): 27 (2010)
Issue (Month): 6 (November)
Pages: 1436-1452

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Handle: RePEc:eee:ecmode:v:27:y:2010:i:6:p:1436-1452

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Web page: http://www.elsevier.com/locate/inca/30411

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Keywords: Statistical premises Substantive information Misspecification Unreliable inferences Untrustworthy empirical evidence Statistical adequacy Substantive adequacy Error-fixing Respecification;

References

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  1. Spanos, Aris, 2009. "The Pre-Eminence of Theory versus the European CVAR Perspective in Macroeconometric Modeling," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy, vol. 3(10), pages 1-14.
  2. Spanos, Aris, 1990. "The simultaneous-equations model revisited : Statistical adequacy and identification," Journal of Econometrics, Elsevier, vol. 44(1-2), pages 87-105.
  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. Spanos, Aris, 1989. "On Rereading Haavelmo: A Retrospective View of Econometric Modeling," Econometric Theory, Cambridge University Press, vol. 5(03), pages 405-429, December.
  5. Anya McGuirk & Aris Spanos, 2009. "Revisiting Error-Autocorrelation Correction: Common Factor Restrictions and Granger Non-Causality," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(2), pages 273-294, 04.
  6. Ricardo, David, 1821. "On the Principles of Political Economy and Taxation," History of Economic Thought Books, McMaster University Archive for the History of Economic Thought, edition 3, number ricardo1821.
  7. 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.
  8. Swamy, P A V B, 1970. "Efficient Inference in a Random Coefficient Regression Model," Econometrica, Econometric Society, vol. 38(2), pages 311-23, March.
  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. 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.
  11. Spanos, Aris & McGuirk, Anya, 2002. "The problem of near-multicollinearity revisited: erratic vs systematic volatility," Journal of Econometrics, Elsevier, vol. 108(2), pages 365-393, June.
  12. 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.
  13. Andreas Koutris & Maria Heracleous & Aris Spanos, 2008. "Testing for Nonstationarity Using Maximum Entropy Resampling: A Misspecification Testing Perspective," Econometric Reviews, Taylor & Francis Journals, vol. 27(4-6), pages 363-384.
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Cited by:
  1. Hanck, Christoph, 2011. "Now, whose schools are really better (or weaker) than Germany's? A multiple testing approach," Economic Modelling, Elsevier, vol. 28(4), pages 1739-1746, July.

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