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Robustness of Logit Analysis: Unobserved Heterogeneity and Mis-specified Disturbances

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  • J. S. Cramer

Abstract

In probit and logit models, the "&bgr;" coefficients vary inversely with the variance of the disturbances. The omission of a relevant orthogonal regressor leads to increased unobserved heterogeneity, and this depresses the "&bgr;" coefficients of the remaining regressors towards zero. For the probit model, Wooldridge ("Econometric Analysis of Cross Section and Panel Data", MIT Press, Cambridge, MA, 2002) has shown that this bias does not carry over to the "effect" of these regressors on the outcome. We find by simulations that this also holds for the logit model, even when omitting a variable leads to severe mis-specification of the disturbance. More simulations show that logit analysis is quite insensitive to pure mis-specification of the disturbance as such. Copyright Blackwell Publishing Ltd and the Department of Economics, University of Oxford 2007.

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Bibliographic Info

Article provided by Department of Economics, University of Oxford in its journal Oxford Bulletin of Economics and Statistics.

Volume (Year): 69 (2007)
Issue (Month): 4 (08)
Pages: 545-555

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Handle: RePEc:bla:obuest:v:69:y:2007:i:4:p:545-555

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  2. Guarino, Cassandra M. & Brown, Abigail B. & Wyse, Adam E., 2011. "Can districts keep good teachers in the schools that need them most?," Economics of Education Review, Elsevier, vol. 30(5), pages 962-979, October.
  3. Esmeralda A. Ramalho & Joaquim J. S. Ramalho, 2009. "Is neglected heterogeneity really an issue in binary and fractional regression models? A simulation exercise for logit, probit and loglog models," CEFAGE-UE Working Papers 2009_10, University of Evora, CEFAGE-UE (Portugal).
  4. Rahmouni, Mohieddine & Ayadi, Mohamed & YIldIzoglu, Murat, 2010. "Characteristics of innovating firms in Tunisia: The essential role of external knowledge sources," Structural Change and Economic Dynamics, Elsevier, vol. 21(3), pages 181-196, August.
  5. Cherchi, Elisabetta & Guevara, Cristian Angelo, 2012. "A Monte Carlo experiment to analyze the curse of dimensionality in estimating random coefficients models with a full variance–covariance matrix," Transportation Research Part B: Methodological, Elsevier, vol. 46(2), pages 321-332.
  6. Geishecker, Ingo & Riedl, Maximilian, 2012. "Ordered response models and non-random personality traits: Monte Carlo simulations and a practical guide," Center for European, Governance and Economic Development Research Discussion Papers 116, University of Goettingen, Department of Economics.
  7. Hong il Yoo, 2012. "The perceived unreliability of rank-ordered data: an econometric origin and implications," Discussion Papers 2012-46, School of Economics, The University of New South Wales.

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