Course Description
Outline
- Static models
- GMM and likelihood approaches. Unobserved heterogeneity. Error components. Specification tests. Error in variables.
- Time series models
- Covariance structures with error components. Autoregressive models with individual effects. Identification and unit roots. Models with stationarity restrictions.
- Dynamic regression models
- Strict exogeneity and predetermined variables. Partial adjustment. Estimation methods. Multiple individual effects.
- Binary choice
- Unobserved heterogeneity in non-linear models. Conditional logit. Random effects probit. Dynamic discrete choice. Bias-corrected fixed effects estimation.