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Course, academic year 2022/2023
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Econometrics II - JEB110
Title: Econometrics II
Guaranteed by: Institute of Economic Studies (23-IES)
Faculty: Faculty of Social Sciences
Actual: from 2022
Semester: winter
E-Credits: 6
Examination process: winter s.:
Hours per week, examination: winter s.:2/2, Ex [HT]
Capacity: 97 / 97 (100)
Min. number of students: unlimited
Virtual mobility / capacity: no
State of the course: taught
Language: English
Teaching methods: full-time
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: Mgr. Barbara Pertold-Gebicka, M.A., Ph.D.
Teacher(s): Mgr. Ing. Kseniya Bortnikova
Jan Mošovský, M.Sc.
Mgr. Barbara Pertold-Gebicka, M.A., Ph.D.
Dipl.-Ing. Mathieu Petit, B.Sc.
MA Salim Turdaliev
Class: Courses for incoming students
Pre-requisite : JEB109
Is pre-requisite for: JEB125
Annotation -
Last update: Mgr. Barbara Pertold-Gebicka, M.A., Ph.D. (04.10.2022)
The objective of the course is to teach students how to use econometric methods to identify and quantify economic relations, how to deal with the data and interpret the results. Together with Econometrics I, the course will prepare students to carry out independent empirical projects (e.g. for a bachelor thesis) and to take the Advanced Econometrics course.

LINK to course in MS Teams:
Syllabus -
Last update: Mgr. Barbara Pertold-Gebicka, M.A., Ph.D. (22.09.2022)

Detailed course contents:

Lecture 1: Introductory Lecture: Repetition of basic econometrics (Chapters 1 - 9)

Lecture 2,3,4: Time Series (Chapters 10 - 12)
Basic Regression analysis with time series data.
- Properties of OLS with time series data.
- Trends and seasonality.
- Stationarity, nonstatinarity and weak dependence.
- Serial correlation and heteroskedasticity in time series regressions.

Lecture 5,6: Panel Data (Chapters 13 - 14)
- Pooling cross sections across time: Simple panel data methods.
- Fixed effects estimation
- Random Effects Models

Lecture 7: Instrumental Variables & 2SLS (Chapter 15)
- Instrumental variables estimation.
- Two Stage Least Squares (2SLS).

Lecture 8: Midterm exam

Lecture 9:  Simultaneous equations (Chapter 16)
- Simultaneous equations models (simultaneity bias in OLS, etc.).

Lecture 10, 11 Limited Dependent Variable models (Chapter 17)
- Binary response models (linear probability, logit, probit).
- Corner solution, censored and truncated data models.

Lecture 12: Repetition (Chapters 10-17)

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