Cramer J.S. Logit Models from Economics and other Fields

Cambridge: Cambridge University Press, 2003. — 180 p. — ISBN: 978-0-511-07354-0.Logistic models are widely used in economics and other disciplines and are easily available as part of many statistical software packages. This text for graduates, practitioners and researchers in economics, medicine and statistics, which was originally published in 2003, explains the theory underlying logit analysis and gives a thorough explanation of the technique of estimation. The author has provided many empirical applications as illustrations and worked examples. A large data set - drawn from Dutch car ownership statistics - is provided online for readers to practise the techniques they have learned. Several varieties of logit model have been developed independently in various branches of biology, medicine and other disciplines. This book takes its inspiration from logit analysis as it is practised in economics, but it also pays due attention to developments in these other fields.
Written not only for economists but for those working in statistics, social sciences and medicine
Many worked examples
Large online data set for readers to practise learned techniquesThe role of the logit model
Plan of the bookand further reading
Program packages and a data set
Notation
The binary model
The logit model for a single attribute
Justification of the model
The latent regression equation; probit and logit
Applications
Maximum likelihood estimation of the binary logit model
Principles of maximum likelihood estimation
Sampling considerations
Estimation of the binary logit model
Consequences of a binary covariate
Estimation from categorical data
Private car ownership and household income
Further analysis of private car ownership
Some statistical tests and measures of fit
Statistical tests in maximum likelihood theory
The case of categorical covariates
The Hosmer–Lemeshow test
Some measures of fit
Outliers, misclassification of outcomes, and omitted variables
Detection of outliers
Misclassification of outcomes
The effect of omitted variables
Analyses of separate samples
A link with discriminant analysis
One-sided sample reduction
State-dependent sampling
Case–control studies
The standard multinomial logit model
Ordered probability models
The standard multinomial logit model
ML estimation of multinomial models: generalities
Estimation of the standard multinomial logit
Multinomial analysis of private car ownership
A test for pooling states
Discrete choice or random utility models
The general logit model
McFadden’s model of random utility maximization
The conditional logit model
Choice of a mode of payment
Models with correlated disturbances
The origins and development of the logit model
The origins of the logistic function
The invention of probit and the advent of logit
Other derivations