Course Description

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.