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Quantitative Methods II - JEB164

Title:	Quantitative Methods II
Czech title:	Kvantitativní metody II
Form of teaching:	lecture+practicals
Guaranteed by:	Institute of Economic Studies (23-IES)
Faculty:	Faculty of Social Sciences
Actual:	from 2026
Duration in semesters:	1
Semester:	summer
E-Credits:	5
Examination process:	summer s.:
Hours per week, examination:	summer s.:1/1, Ex [HT]
Capacity:	24 / 24 (20)
Maximum number of enrolled students:	20
Min. number of students:	unlimited
4EU+:	no
Virtual mobility / capacity:	no
State of the course:	taught
Language:	English
Teaching methods:	full-time
Repeated enrollment:	2 / 2 / 1 / 2
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. Václav Brož, Ph.D.
Teacher(s):	Mgr. Václav Brož, Ph.D.
Class:	Courses for incoming students
Pre-requisite :	{Group of pre-requisites for JEB164 (JPB049 or JPB098)}

Noticeboard

Annotation -

This course provides a hands-on introduction to applied econometrics used in academic research and policy analysis. We will focus on modern tools: regression diagnostics, causal inference, discrete choice models, time series, panel data, and R-based empirical workflows.

Please note that this course is not suitable for 1st year PPE students - please sign up for the course in 2027.

Last update: Brož Václav, Mgr., Ph.D. (04.02.2026)

Aim of the course -

The goal of this course is to equip students with the skills to apply econometric methods in their bachelor theses. In addition, the course provides a solid foundation for more advanced econometrics courses, such as those offered at the master’s level.

Last update: Brož Václav, Mgr., Ph.D. (04.12.2025)

Course completion requirements -

The final grade for completing the course will be based on the following criteria:

· Attendance + loading data beforehand (see emails for details): 10%

· First individual homework: 25%

· Three group homework: 15% each (45% in total)

· Final individual assignment (bachelor thesis proposal, bachelor thesis submission or replication study): 20%

There will be opportunities to consult with the teacher your bachelor thesis proposal., including the targeted teachers (e.g., from the IES).

Please note that once a student begins fulfilling course requirements, they will receive a grade at the end of the semester. The final grade will be determined by the student’s percentage performance according to the following scale:

90.01–100.00% (A)
80.01–90.00% (B)
70.01–80.00% (C)
60.01–70.00% (D)
50.01–60.00% (E)
0.00–50.00% (F)

We will use generative AI to help us with coding and AI use will be also allowed for homework. However, it is essential that you understand how the code works. Still, any plagiarism, including the use of artificial intelligence in violation of Charles University’s recommendations or in situations where it is not explicitly permitted, will result in a grade reduction, failure of the course, or disciplinary proceedings.

Last update: Brož Václav, Mgr., Ph.D. (16.03.2026)

Literature -

Koop, Gary. Introduction to econometrics. John Wiley & Sons, 2008.

Please note that there is a copy at the CERGE-EI library that you can borrow.

Last update: Brož Václav, Mgr., Ph.D. (26.08.2025)

Teaching methods -

There will be 6 sessions during the semester, each lasting 2 × 80 minutes (160 minutes in total). Each session will consist of a lecture and a hands-on seminar. Students are expected to bring their laptops with RStudio installed and ready to use. All course materials - including the submission of homework assignments - will be managed via Moodle (please enroll here: https://dl2.cuni.cz/course/view.php?id=6182).

We will use R, RStudio, and packages including: tidyverse, AER, fixest, plm, margins, nnet, sandwich, strucchange, forecast, tidysynth.

Last update: Brož Václav, Mgr., Ph.D. (04.12.2025)

Syllabus -

1. Regression Extensions

· F-tests

· Dummy variables & functional forms (logs, quadratics)

· Omitted variable bias & adjusted R²

· Heteroskedasticity

· Instrumental Variables

2. Discrete Choice & Limited Dependent Variables

· Probit, logit, marginal effects, odds ratios

· Multinomial logit & baseline comparisons

· Poisson regression for count data

3. + 4 . Time Series Essentials

· Autocorrelation function & autoregressive models; information criteria

· Random walk & Dickey–Fuller tests

· Basic forecasting

· Cointegration

· Structural breaks

· Principal components analysis

5. + 6. Panel Data & Causal Methods

· Pooled OLS, fixed effects, random effects; Hausman test

· Difference-in-Differences

· Synthetic Control Method

Last update: Brož Václav, Mgr., Ph.D. (04.12.2025)

Learning outcomes -

· Selecting appropriate econometric methods for empirical questions usable not only for bachelor theses

· Diagnosing model assumptions and applying robust solutions

· Estimating instrumental variables, discrete choice, panel, time-series, and causal models

· Producing fully reproducible R code and workflows

Last update: Brož Václav, Mgr., Ph.D. (04.12.2025)

Course registration requirements -

The course Quantitative Methods I - JPB049 (https://is.cuni.cz/studium/eng/predmety/index.php?do=predmet&kod=JPB049&skr=2025) is a recommended if not strictly required prerequisite.

The first individual homework will aim to bring all students to the same level as for working in RStudio (data visualization, data management), fundamentals of statistics (inference, normal distribution, confidence intervals, t-test, correlations) and econometrics (including basics of multiple linear regression) to be able to complete this course.

You can also contact Dr. Lukáš Hájek (lukas.hajek@fsv.cuni.cz) as the teacher of the Quantitative Methods I for access for materials from this course if you like to take QM II but did not take QM I.

Last update: Brož Václav, Mgr., Ph.D. (05.01.2026)