Revisiting the omitted variables argument: Substantive vs. statistical adequacy
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.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 13 (2006)
Issue (Month): 2 ()
|Contact details of provider:|| Web page: http://www.tandfonline.com/RJEC20|
|Order Information:||Web: http://www.tandfonline.com/pricing/journal/RJEC20|
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- repec:cup:cbooks:9780521269124 is not listed on IDEAS
- Spanos, Aris, 1989. "On Rereading Haavelmo: A Retrospective View of Econometric Modeling," Econometric Theory, Cambridge University Press, vol. 5(03), pages 405-429, December.
- Spanos, Aris, 1990. "The simultaneous-equations model revisited : Statistical adequacy and identification," Journal of Econometrics, Elsevier, vol. 44(1-2), pages 87-105.
- Spanos, Aris, 1995. "On theory testing in econometrics : Modeling with nonexperimental data," Journal of Econometrics, Elsevier, vol. 67(1), pages 189-226, May.
- 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.
When requesting a correction, please mention this item's handle: RePEc:taf:jecmet:v:13:y:2006:i:2:p:179-218. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Michael McNulty)
If references are entirely missing, you can add them using this form.