The New Palgrave Dictionary of Economics

2018 Edition
| Editors: Macmillan Publishers Ltd

Categorical Data

  • A. Colin Cameron
Reference work entry
DOI: https://doi.org/10.1057/978-1-349-95189-5_2417

Abstract

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.

Keywords

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 

JEL Classifications

C25 
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Copyright information

© Macmillan Publishers Ltd. 2018

Authors and Affiliations

  • A. Colin Cameron
    • 1
  1. 1.