Arbeitspapier
Categorical data
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.
- Language
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Englisch
- Bibliographic citation
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Series: Working Paper ; No. 06-12
- Classification
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Wirtschaft
Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
Single Equation Models; Single Variables: Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
- Event
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Geistige Schöpfung
- (who)
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Cameron, A. Colin
- Event
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Veröffentlichung
- (who)
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University of California, Department of Economics
- (where)
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Davis, CA
- (when)
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2006
- Handle
- Last update
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10.03.2025, 11:45 AM CET
Data provider
This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Cameron, A. Colin
- University of California, Department of Economics
Time of origin
- 2006
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Analyzing ordered categorical data derived from elliptically symmetric distributions
Supplement to Structured additive regression for categorical space-time data: a mixed model approach
Identification and Estimation of Categorical Random Coefficient Models
Estimating ordered categorical variables using panel data: a generalized ordered probit model with an autofit procedure
The application of local indicators for categorical data (LICD) in the spatial analysis of economic development
Cycling and Categorical Learning in Decentralized Adverse Selection Economies
Analyzing categorical data
Parametric and Semiparametric Estimation in Models with Misclassified Categorical Dependent Variables
Bayesian Clustering of Categorical Time Series Using Finite Mixtures of Markov Chain Models
The Empirics of Regulatory Reforms Proxied by Categorical Variables: Recent Findings and Methodological Issues
Categorical Forecasts and Non-Categorical Loss Functions
Measuring Health Inequality with Categorical Data: Some Regional Patterns
Analyzing ordered categorical data derived from elliptically symmetric distributions
Supplement to Structured additive regression for categorical space-time data: a mixed model approach
Identification and Estimation of Categorical Random Coefficient Models
Estimating ordered categorical variables using panel data: a generalized ordered probit model with an autofit procedure
The application of local indicators for categorical data (LICD) in the spatial analysis of economic development
Cycling and Categorical Learning in Decentralized Adverse Selection Economies
Analyzing categorical data
Parametric and Semiparametric Estimation in Models with Misclassified Categorical Dependent Variables
Bayesian Clustering of Categorical Time Series Using Finite Mixtures of Markov Chain Models