Linear Probability, Logit, and Probit Models

✍ Scribed by John H. Aldrich, Forrest D. Nelson

• Publisher: Sage Publications
• Year: 1984
• Tongue: English
• Leaves: 95
• Category: Library

✦ Table of Contents

Contents
Series Introduction
Acknowledgments
1. The Linear Probability Model
1.0 Introduction
1.1 Review of the Multivariate, Linear Regression Model
1.2 A Dichotomous Dependent Variable and the Linear Probability Model
1.3 A Dichotomous Response Variable with Replicated Data
1.4 Polytomous or Multiple Category Dependent Variables
1.5 The Linearity Assumption
1.6 The Effect of an Incorrect Linearity Assumption
2. Specification of Nonlinear Probability Models
2.0 Introduction
2.1 The General Problem of Specification
2.2 Alternative Nonlinear Functional Forms for the Dichotomous Case
2.3 Derivation of Nonlinear Transformations from a Behavioral Model
2.4 Nonlinear Probability Specifications for Polytomous Variables
2.5 Behavior of the Logit and Probit Specifications
2.6 Summary
3. Estimation of Probit and Logit Models for Dichotomous Dependent Variables
3.0 Introduction
3.1 Assumptions of the Models
3.2 Maximum Likelihood Estimation
3.3. Properties of Estimates
3.4 Interpretation of and Inference from MLE Results
3.5 Conclusions
4. Minimum Chi-Square Estimation and Polytomous Models
4.0 Introduction
4.1 Minimum Chi-Square Estimation for Replicated, Dichotomous Data
4.2. Polytomous Dependent Variables
5. Summary and Extensions
5.0 Introduction
5.1 Summary
5.2 Extensions
Notes
References
About the Authors