Course contents (about 100 words) (Include laboratory/design activities):
1. Review of Classical Linear Regression Model:
Gauss-Markov assumptions, finite sample properties, large sample properties
2. Instrumental Variable Estimation:
Motivation for instrumentation, Simultaneity Bias, Endogeneity and Measurement Error; IV Estimation; 2SLS Estimation
3. Generalized Method of Moments:
Single equation linear GMM
4. Systems of Equations
Seemingly Unrelated Regressions (SUR) model; Simultaneous Equations Models: Identification
5. Panel Data models:
Pooled Estimation; Unobserved Heterogeneity: Fixed vs. Random Effects; ML vs. GMM estimation
6. Discrete Choice Models:
Binary response models, Multinomial Response Models, Ordered Response Models
7. Censored Regression Models:
Estimation and Inference with Censored Tobit
8. Estimating Average Treatment Effects:
Regression Methods, Methods Based on the Propensity Score, Estimating the ATE Using IV
The information provided here may not be updated. Please check UG/PG section for updated course offering data.
