Local Sensitivity and Diagnostic Tests

27 Pages Posted: 8 Mar 2007

See all articles by J.R. Magnus

J.R. Magnus

Vrije Universiteit Amsterdam, School of Business and Economics

Andrey L. Vasnev

University of Sydney

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In this paper, we confront sensitivity analysis with diagnostic testing. Every model is misspecified (in the sense that no model coincides with the data-generating process), but a model is useful if the parameters of interest (the focus) are not sensitive to small perturbations in the underlying assumptions. The study of the effect of these violations on the focus is called sensitivity analysis. Diagnostic testing, on the other hand, attempts to find out whether a nuisance parameter is (statistically) 'large' or 'small'. Both aspects are important, but traditional applied econometrics tends to use only diagnostics and forget about sensitivity analysis. We develop a theory of sensitivity in a maximum likelihood framework, give conditions under which the diagnostic and the sensitivity are asymptotically independent, and demonstrate with three core examples that this independence is the rule rather than the exception, thus underlying the importance of sensitivity analysis.

Suggested Citation

Magnus, Jan R. and Vasnev, Andrey L., Local Sensitivity and Diagnostic Tests. Econometrics Journal, Vol. 10, No. 1, pp. 166-192, March 2007. Available at SSRN: https://ssrn.com/abstract=969142 or http://dx.doi.org/10.1111/j.1368-423X.2007.00204.x

Jan R. Magnus (Contact Author)

Vrije Universiteit Amsterdam, School of Business and Economics ( email )

De Boelelaan 1105
Amsterdam, 1081HV

Andrey L. Vasnev

University of Sydney ( email )

Sydney, NSW 2006

HOME PAGE: http://www.econ.usyd.edu.au/staff/andreyv

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