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Categorical Data

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  • A. Colin Cameron

    (Department of Economics, University of California Davis)

Abstract

A very brief survey of regression for categorical data. Categorical outcome (or discrete outcome or qualitative response) regression models are models for a discrete dependent variable recording in which of two or more categories an outcome of interest lies. For binary data (two categories) probit and logit models or semiparametric methods are used. For multinomial data (more than two categories) that are unordered, common models are multinomial and conditional logit, nested logit, multinomial probit, and random parameters logit. The last two models are estimated using simulation or Bayesian methods. For ordered data, standard multinomial models are ordered logit and probit, or count models are used if ordered discrete data are actually a count.

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Bibliographic Info

Paper provided by University of California, Davis, Department of Economics in its series Working Papers with number 612.

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Length: 8
Date of creation: 02 Mar 2006
Date of revision:
Handle: RePEc:cda:wpaper:06-12

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Related research

Keywords: binary data; multinomial; logit; probit; count data;

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  1. Manski, Charles F., 1975. "Maximum score estimation of the stochastic utility model of choice," Journal of Econometrics, Elsevier, vol. 3(3), pages 205-228, August.
  2. Amemiya, Takeshi, 1981. "Qualitative Response Models: A Survey," Journal of Economic Literature, American Economic Association, vol. 19(4), pages 1483-1536, December.
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