1. Linear Regression 
Revision using matrix algebra, finite sample properties, large-sample properties 
Reading: G(3-5: 26-143), W (4: 49-76) 

2-3. (2.1.) Introduction to Estimation Frameworks in econometrics 
Parametric estimation and inference (likelihood-based methods), semiparametric estimation (GMM, empirical likelihood), properties of estimators 
Reading: G(12: 432-454) 

(2.2.) Quantile Regressions 
Quantile regressions, Quantiles and conditional quantiles
Reading: G(7.3: 202-207) and CT(4.6.) 

(2.3.) Maximum Likelihood estimators 
Basic likelihood concepts, score functions, principle of ML and its properties, Quasi and pseudo-MLE
Reading: G(14: 509-548), W(13: 385-397),
or alternatively CT(5: 116-163), MHH(1,2: 1:82 and 9:313-346 for QMLE) 

4. Generalized Method of Moments 
The method of moments, GMM, properties, testing hypothesis in the GMM framework
Reading: G(13.1.-13.5.: 455-480), W(14: 421-448)  or alternatively CT(6: 166-219), MHH(10:361:396)  

5. Simulation-based estimation and inference  computer-intensive, simulation-based methods, bootstrap, maximum simulated likelihood estimation, moment-based simulation estimation 
Reading: G(15: 603-634)
or alternatively CT (11-13: 357-416, selection) or MHH(12: 447-477)

6. Endogeneity and Instrumental variables 
IV estimation, Multiple Instruments (2SLS), asymptotic theory and robust inference, measurement errors and omitted variables,  
Reading: G(8.1.-8.4. 8.7.: 219-251), W (5: 83-107)
+ Endogeneity in Systems of Equations (G 10.6.-10.7: 314-355?) if time allows 

7. MIDTERM 

8. Generalized Least Squares, non - i.i.d. errors   Generalized regression models and heteroscedasticity (efficient estimation via (F)GLS), Seemingly unrelated regressions 
Reading: G(9.1.-9.3.: 257-266), G(10.1-10.3: 290 - 304), W(7: 143-167) 

9. Models for Panel Data I (static panel data methods)
advantages of panel data; basics of linear panel models; pooled, random effects  and fixed effect models; SUR versus Panel Data Models; target parameters and estimation by GLS; applications.
Reading: G(11: 343-382), W (10: 247-288)   or additional CT(21:697-739)

10. Models for Panel Data II (Dynamic linear paneldata models) 
Extensions of basic models; types of exogeneity; endogenous regressors; dynamic models; Discrete Choice Panel data methods, GMM methods for Panel models;  
Reading: G(11: 382-426), G(13.6.5.: pp493) GMM in panel data, W (11: 299-328)
or additional CT(22: 743-778) 

11. Discrete Choice models  
Review of linear probability models for binary Discrete choice models, advantages, Logit and Probit models, specification issues 
Reading: W (15.2.-15.7.: 451-480), G(17.1. - 17.3.: 681-714)  or additiona CT (14: selected) 

12. Extended Discrete Choice models 
Multinomial logit and conditional logit models, Pooled discrete choice models  Reading: W (15.8.-15.10. : 480-509), G(17.4. - 17.5.: 716-752)
+ 18.1.-18.5.: 769:829 (only selection IF TIME ALLOWS)  additiona CT (14, 23: selected)