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Omitted Variables and Misspecified Disturbances in the Logit Model

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  • J.S. Cramer

    ()
    (University of Amsterdam)

Abstract

In binary discrete regression models like logit or probit the omis-sion of a relevant regressor (even if it is orthogonal) depresses the re-maining b coefficients towards zero. For the probit model, Wooldridge(2002) has shown that this bias does not carry over to the effect ofthe regressor on the outcome. We find by simulations that this alsoholds for logit models, even when the omitted variable leads to severemisspecification of the disturbance. More simulations show that es-timates of these effects by logit analysis are also impervious to puremisspecification of the disturbance.

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Bibliographic Info

Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 05-084/4.

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Date of creation: 15 Sep 2005
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Handle: RePEc:dgr:uvatin:20050084

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Keywords: logit model; omitted variables; misspecification;

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  1. J. S. Cramer, 2004. "Scoring bank loans that may go wrong: a case study," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, Netherlands Society for Statistics and Operations Research, vol. 58(3), pages 365-380.
  2. Cramer,J. S., 2011. "Logit Models from Economics and Other Fields," Cambridge Books, Cambridge University Press, Cambridge University Press, number 9780521188036.
  3. Baltas, George & Doyle, Peter, 2001. "Random utility models in marketing research: a survey," Journal of Business Research, Elsevier, Elsevier, vol. 51(2), pages 115-125, February.
  4. Gourieroux,Christian, 2000. "Econometrics of Qualitative Dependent Variables," Cambridge Books, Cambridge University Press, Cambridge University Press, number 9780521589857.
  5. Yatchew, Adonis & Griliches, Zvi, 1985. "Specification Error in Probit Models," The Review of Economics and Statistics, MIT Press, vol. 67(1), pages 134-39, February.
  6. Gourieroux,Christian, 2000. "Econometrics of Qualitative Dependent Variables," Cambridge Books, Cambridge University Press, Cambridge University Press, number 9780521331494.
  7. Lee, Lung-Fei, 1982. "Specification error in multinomial logit models : Analysis of the omitted variable bias," Journal of Econometrics, Elsevier, Elsevier, vol. 20(2), pages 197-209, November.
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Cited by:
  1. Ambra Poggi & Matteo Richiardi, 2012. "Accounting for Unobserved Heterogeneity in Discrete-time, Discrete-choice Dynamic Microsimulation Models. An application to Labor Supply and Household Formation in Italy," LABORatorio R. Revelli Working Papers Series, LABORatorio R. Revelli, Centre for Employment Studies 117, LABORatorio R. Revelli, Centre for Employment Studies.
  2. Kerekes, Monika, 2009. "Growth miracles and failures in a Markov switching classification model of growth," Discussion Papers 2009/11, Free University Berlin, School of Business & Economics.
  3. Annemiek Vuren & Daniel Vuuren, 2007. "Financial Incentives in Disability Insurance in the Netherlands," De Economist, Springer, Springer, vol. 155(1), pages 73-98, March.
  4. Massimiliano Bratti & Daniele Checchi & Guido de Blasio, 2008. "Does the Expansion of Higher Education Increase the Equality of Educational Opportunities? Evidence from Italy," LABOUR, CEIS, CEIS, vol. 22(s1), pages 53-88, 06.
  5. Matteo Richiardi & Ambra Poggi, 2012. "Imputing Individual Effects in Dynamic Microsimulation Models. An application of the Rank Method," Carlo Alberto Notebooks, Collegio Carlo Alberto 267, Collegio Carlo Alberto.
  6. Dalit Contini & Andrea Scagni, 2011. "Inequality of opportunity in secondary school enrolment in Italy, Germany and the Netherlands," Quality & Quantity: International Journal of Methodology, Springer, Springer, vol. 45(2), pages 441-464, February.

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