Statistical adequacy and the trustworthiness of empirical evidence: Statistical vs. substantive information
AbstractThe 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.
Download InfoIf 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.
Bibliographic InfoArticle provided by Elsevier in its journal Economic Modelling.
Volume (Year): 27 (2010)
Issue (Month): 6 (November)
Contact details of provider:
Web page: http://www.elsevier.com/locate/inca/30411
Statistical premises Substantive information Misspecification Unreliable inferences Untrustworthy empirical evidence Statistical adequacy Substantive adequacy Error-fixing Respecification;
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.:
- 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.
- Aris Spanos, 2001. "Revisiting data mining: 'hunting' with or without a license," Journal of Economic Methodology, Taylor and Francis Journals, vol. 7(2), pages 231-264.
- Aris Spanos, 2006. "Revisiting the omitted variables argument: Substantive vs. statistical adequacy," Journal of Economic Methodology, Taylor and Francis Journals, vol. 13(2), pages 179-218.
- Andreas Koutris & Maria Heracleous & Aris Spanos, 2008. "Testing for Nonstationarity Using Maximum Entropy Resampling: A Misspecification Testing Perspective," Econometric Reviews, Taylor and Francis Journals, vol. 27(4-6), pages 363-384.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Spanos, Aris, 1995. "On theory testing in econometrics : Modeling with nonexperimental data," Journal of Econometrics, Elsevier, vol. 67(1), pages 189-226, May.
- 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.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Wendy Shamier).
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.