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Course, academic year 2019/2020
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Advanced Econometrics - JEM005
Title in English: Advanced Econometrics
Guaranteed by: Institute of Economic Studies (23-IES)
Faculty: Faculty of Social Sciences
Actual: from 2014
Semester: winter
Points: 6
E-Credits: 6
Examination process: winter s.:
Hours per week, examination: winter s.:2/2 Ex [hours/week]
Capacity: 152 / 114 (152)
Min. number of students: unlimited
State of the course: taught
Language: English
Teaching methods: full-time
Note: course can be enrolled in outside the study plan
enabled for web enrollment
priority enrollment if the course is part of the study plan
Guarantor: doc. PhDr. Jozef Baruník, Ph.D.
Teacher(s): doc. PhDr. Jozef Baruník, Ph.D.
Mgr. Martin Hronec
Mgr. Josef Kurka
Mgr. Ing. Matěj Nevrla
Annotation -
Last update: doc. PhDr. Jozef Baruník, Ph.D. (25.09.2017)
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.
Literature -
Last update: doc. PhDr. Jozef Baruník, Ph.D. (25.09.2017)

For the topics covered during the semester, we will use chapters mainly from the two textbooks:

(G) Greene, W.H. Econometric Analysis, 7th edition, Prentice Hall, 2012
 please note that older versions of the text are fine too, just be aware of different chapters numbering

(W) Wooldridge, J. Econometric Analysis of Cross-Section and Panel Data, Boston: MIT Press, 2010, 2nd edition

In addition, following textbook can be used
(CT) Cameron, C. and Trivedi, P.K. Microeconomertics: Methods and Applications, Cambridge University Press, New York, May 2005

Other good references:
Maddala, Limited-dependent and Qualitative Variables in Econometrics, Cambridge, 1982.
Davidson and MacKinnon: Econometric Theory and Methods, Oxford, 2003.
Hayashi, F., Econometrics, Princeton: Princeton Univ. Press, 2000.
Gourieroux and Monfort: Statistics and Econometric Models: Vol 1 and 2 (Cambridge University Press), 1995
(MHH) Martin, Hurn and Harris: Econometric Modelling with Time Series: Specification, Estimation and Testing, Cambridge University Press, 2013
Hansen, Bruce E.: Econometrics (online draft of graduate textbook), University of Wisconsin (last revision: January 2013)

The Davidson and MacKinnon text is a good up-to-date text. Maddala’s text, and the Wooldridge text are excellent for limited dependent and qualitative variables. Hayashi (2002) is much more involved with time series econometrics. Gourieroux and Monfort (1995) is an excellent complementary book for students interested in more technical text (at PhD level). Verbeek is less technical and undergraduate level. A new and rather broad text is Martin, Hurn and Harris (2013).

Requirements to the exam -
Last update: doc. PhDr. Jozef Baruník, Ph.D. (25.09.2017)

Assignments: 0 - 15%
Midterm Exam: 0 - 20% 
Final Exam: 0 - 50% (to pass the Final, one needs to have at least 60% of correct answers)
Empirical Paper: 15%

Syllabus -
Last update: doc. PhDr. Jozef Baruník, Ph.D. (25.09.2017)

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)

 
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