Sylabus
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 quantilesReading: 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)
Anotace
The objective of the course is to help students understand several important modern techniques in econometrics and apply them in empirical research and practical applications. Emphasis of the course will be placed on understanding the essentials underlying the core techniques, and developing the ability to relate the methods to important issues faced by a practicioner.
By completing this course, students will be able to use a computer based statistical software to analyze the data, choose appropriate models and estimators for given economic application, understand and interpret the results in detail (diagnose problems, understand proper inference) and will be confident to carry out the analysis and conclusions with respect to appropriatness and limitation of the methodology used. Finally, students will have sufficient grounding in econometric theory to begin advanced work in the field.