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Revisiting the omitted variables argument: Substantive vs. statistical adequacy

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  • Aris Spanos

Abstract

The problem of omitted variables is commonly viewed as a statistical misspecification issue which renders the inference concerning the influence of X t on yt unreliable, due to the exclusion of certain relevant factors W t . That is, omitting certain potentially important factors W t may confound the influence of X t on yt . The textbook omitted variables argument attempts to assess the seriousness of this unreliability using the sensitivity of the estimator [image omitted] � to the inclusion/exclusion of W t , by tracing that effect to the potential bias/inconsistency of [image omitted] � . It is argued that the confounding problem is one of substantive inadequacy in so far as the potential error concerns subject-matter, not statistical, information. Moreover, the textbook argument in terms of the sensitivity of point estimates provides a poor basis for addressing the confounding problem. The paper reframes the omitted variables question into a hypothesis testing problem, supplemented with a post-data evaluation of inference based on severe testing. It is shown that this testing perspective can deal effectively with assessing the problem of confounding raised by the omitted variables argument. The assessment of the confouding effect using hypothesis testing is related to the conditional independence and faithfulness assumptions of graphical causal modeling.

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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Journal of Economic Methodology.

Volume (Year): 13 (2006)
Issue (Month): 2 ()
Pages: 179-218

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Handle: RePEc:taf:jecmet:v:13:y:2006:i:2:p:179-218

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Related research

Keywords: omitted variables; bias/inconsistency; confounding; robustness; sensitivity analysis; misspecification; substantive vs. statistical adequacy; structural vs. statistical models; severe testing;

References

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  1. Spanos, Aris, 1995. "On theory testing in econometrics : Modeling with nonexperimental data," Journal of Econometrics, Elsevier, vol. 67(1), pages 189-226, May.
  2. repec:cup:cbooks:9780521424080 is not listed on IDEAS
  3. Spanos, Aris, 1989. "On Rereading Haavelmo: A Retrospective View of Econometric Modeling," Econometric Theory, Cambridge University Press, vol. 5(03), pages 405-429, December.
  4. Spanos,Aris, 1986. "Statistical Foundations of Econometric Modelling," Cambridge Books, Cambridge University Press, number 9780521269124, October.
  5. Spanos, Aris, 1990. "The simultaneous-equations model revisited : Statistical adequacy and identification," Journal of Econometrics, Elsevier, vol. 44(1-2), pages 87-105.
  6. Leamer, Edward E & Leonard, Herman B, 1983. "Reporting the Fragility of Regression Estimates," The Review of Economics and Statistics, MIT Press, vol. 65(2), pages 306-17, May.
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Citations

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Cited by:
  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. Chatelain, Jean-Bernard & Ralf, Kirsten, 2012. "Spurious Regressions and Near-Multicollinearity, with an Application to Aid, Policies and Growth," MPRA Paper 42533, University Library of Munich, Germany.
  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.
  4. Spanos, Aris, 2008. "The 'Pre-Eminence of Theory' versus the 'General-to-Specific' Cointegrated VAR Perspectives in Macro-Econometric Modeling," Economics Discussion Papers 2008-25, Kiel Institute for the World Economy.
  5. 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.
  6. Francisco Estrada & Víctor Guerrero & Carlos Gay-García & Benjamín Martínez-López, 2013. "A cautionary note on automated statistical downscaling methods for climate change," Climatic Change, Springer, vol. 120(1), pages 263-276, September.

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