Microeconometrics
Methods and Applications

This text is the most comprehensive work to date on microeconometrics, its methods and applications.

A. Colin Cameron (Author), Pravin K. Trivedi (Author)

9780521848053, Cambridge University Press

Hardback, published 9 May 2005

1056 pages, 43 b/w illus. 101 tables
26.3 x 18.5 x 4.4 cm, 1.78 kg

'… it is well organised and well written … the authors are to be congratulated on this sure-footed addition to the econometrics literature.' The Times Higher Education Supplement

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.

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.

Subject Areas: Applied mathematics [PBW], Economic statistics [KCHS], Econometrics [KCH]