Microeconometrics: Methods and Applications

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.