The New Palgrave Dictionary of Economics

2018 Edition
| Editors: Macmillan Publishers Ltd

Categorical Data

  • A. Colin Cameron
Reference work entry


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.


Additive random utility model (ARUM) Binary outcomes Categorical data Categorical outcome models Choice-based sampling Cumulative distribution function (CDF) Discrete outcome models: see categorical outcome models Heteroskedasticity Limited dependent variable models Logit models Maximum likelihood Maximum score methods Multinomial models Probit models Qualitative response models: see categorical outcome models Random parameters logit model Semiparametric estimation Simulation-based estimation Tobit models 

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  1. Amemiya, T. 1981. Qualitative response models: A survey. Journal of Economic Literature 19: 1483–1536.Google Scholar
  2. Amemiya, T. 1985. Advanced econometrics. Cambridge, MA: Harvard University Press.Google Scholar
  3. Cameron, A., and P. Trivedi. 2005. Microeconometrics: Methods and applications. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  4. Greene, W. 2003. Econometric analysis. 5th ed. Upper Saddle River: Prentice-Hall.Google Scholar
  5. Maddala, G. 1983. Limited-dependent and qualitative variables in econometrics. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  6. Manski, C. 1975. The maximum score estimator of the stochastic utility model of choice. Journal of Econometrics 3: 205–228.CrossRefGoogle Scholar
  7. Manski, C., and D. McFadden, ed. 1981. Structural analysis of discrete data with econometric applications. Cambridge, MA: MIT Press.Google Scholar
  8. McFadden, D. 1974. Conditional logit analysis of qualitative choice behavior. In Frontiers in econometrics, ed. P. Zarembka. New York: Academic Press.Google Scholar
  9. Pagan, A., and A. Ullah. 1999. Nonparametric econometrics. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  10. Train, K. 2003. Discrete choice methods with simulation. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  11. Wooldridge, J. 2002. Econometric analysis of cross section and panel data. Cambridge, MA: MIT Press.Google Scholar

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© Macmillan Publishers Ltd. 2018

Authors and Affiliations

  • A. Colin Cameron
    • 1
  1. 1.