Microeconometrics : methods and applications
著者
書誌事項
Microeconometrics : methods and applications
Cambridge University Press, 2005
- : hardback
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注記
Includes bibliographical references (p. 961-997) and indexes
内容説明・目次
内容説明
This book provides the most comprehensive treatment to date of microeconometrics, the analysis of individual-level data on the economic behavior of individuals or firms using regression methods for cross section and panel data. The book is oriented to the practitioner. A basic understanding of the linear regression model with matrix algebra is assumed. The text can be used for a microeconometrics course, typically a second-year economics PhD course; for data-oriented applied microeconometrics field courses; and as a reference work for graduate students and applied researchers who wish to fill in gaps in their toolkit. Distinguishing features of the book include emphasis on nonlinear models and robust inference, simulation-based estimation, and problems of complex survey data. The book makes frequent use of numerical examples based on generated data to illustrate the key models and methods. More substantially, it systematically integrates into the text empirical illustrations based on seven large and exceptionally rich data sets.
目次
- 1. Introduction
- 2. Causal and non-causal models
- 3. Microeconomic data structures
- 4. Linear models
- 5. ML and NLS estimation
- 6. GMM and systems estimation
- 7. Hypothesis tests
- 8. Specification tests and model selection
- 9. Semiparametric methods
- 10. Numerical optimization
- 11. Bootstrap methods
- 12. Simulation-based methods
- 13. Bayesian methods
- 14. Binary outcome models
- 15. Multinomial models
- 16. Tobit and selection models
- 17. Transition data: survival analysis
- 18. Mixture models and unobserved heterogeneity
- 19. Models of multiple hazards
- 20. Models of count data
- 21. Linear panel models: basics
- 22. Linear panel models: extensions
- 23. Nonlinear panel models
- 24. Stratified and clustered samples
- 25. Treatment evaluation
- 26. Measurement error models
- 27. Missing data and imputation
- A. Asymptotic theory
- B. Making pseudo-random draw.
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