Advanced Econometrics II 2400-M2IiEZEKO

The course instructor presents theoretical introduction and some empirical or didactic examples to each topic while students prepare own projects with the use of the selected models and methods at home.

The course approach is based on the presumption of primacy of practice over theory. Theoretical introduction abstracts from formal proofs and derivation and is designed as to make students ready to read empirical articles based on the discussed methods and use them in practice.

General methods
1. Simulation and sampling methods
2. Nonparametric estimation
3. Bayesian econometrics

Time-series
4. State-space models and Kalman filter
5. Markov switching models
6. Models with rational expectations
7. Threshold and smooth transition models (*)
8. Spectral analysis (*)

Cross-section
9. Duration and survival analysis
10. Propensity score matching
11. Quantile regression
12. Generalized linear models (*)
13. Regression discontinuity design (*)

Panel data
14. Panel discrete choice models
15. Hausman-Taylor model
16. Dynamic panel models
17. Panel tests for unit roots and cointegration (*)
18. Panel VAR models (*)

Type of course

obligatory courses

Prerequisites (description)

(in Polish) Rachunek prawdopodobieństwa: zmienna losowa, rozkład zmiennej losowej, rozkłąd warunkowy zmiennej losowej, warunkowa wartość oczekiwana zmiennej losowej, wariancja zmiennej losowej, pojęcie zbieżności według prawdopodobieństwa i rozkładu, twierdzenie Bayesa. Statystyka matematyczna: model statystyczny, estymacja parametrów modelu MNW, twierdzenia graniczne, własności asymptotyczne estymatorów, wnioskowanie statystyczne. Ekonometria: model ekonometryczny, MNK, KMRL, egzogeniczność, modele dla dyskretnej zmiennej zależnej, modele wielorównaniowe, identyfikacja, modele autoregresyjne. Rachunek prawdopodobieństwa Statystyka opisowa Statystyka matematyczna Ekonometria podstawowa Ekonometria zaawansowana

Course coordinators

Łukasz Postek

Learning outcomes

Student should be familiarized with the most important models and methods in the contemporary econometrics. Student should be ready to read empirical articles based on these methods and use them in practice.

Assessment criteria

2/3 econometric models + 1/3 exam
econometric models – two projects on real data prepared individually or in a two-person team; each project from a different group of subjects (general, cross-section, time-series, panel data). The project should be a simplified econometric exercise similar to the one found in an article from A list by Ministry of Science and Higher Education (other projects are also possible byt need earlier acceptance). The project should take a form of a short econometric report. No requirements regarding econometric software.
exam – 4-5 open questions / exercises of general nature (no proofs or derivations) which aim to verify:
- practical skills in interpretation of the most fundamental results (tables / figures) of the covered econometric models and methods
- fundamental knowledge in the field of the covered econometric models and methods
The exam is designed for 60 minutes in the stationary form. However, should the exam session be held online, the exact form and technical details will be tailored as to fit the guidelines provided by the WNE / UW authorities.

Bibliography

Slides and codes prepared by the course instructor
Greene, William H. (2012): Econometric Analysis, Pearson.
Hamilton, James D. (1994): Time Series Analysis, Princeton University Press.
Koop, Garry (2003): Bayesian Econometrics, Wiley.
Wooldridge, Jeffrey D. (2010): Econometric Analysis of Cross Section and Panel Data, Second Edition, MIT Press.

Additional information

Information on level of this course, year of study and semester when the course unit is delivered, types and amount of class hours - can be found in course structure diagrams of apropriate study programmes. This course is related to the following study programmes:

Computer Science and Econometrics, full time, second cycle

Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system:

Description of 2400-M2IiEZEKO in USOSweb