PředmětyPředměty(verze: 945)
Předmět, akademický rok 2022/2023
   Přihlásit přes CAS
Introductory Econometrics - JEM062
Anglický název: Introductory Econometrics
Český název: Introductory Econometrics
Zajišťuje: Institut ekonomických studií (23-IES)
Fakulta: Fakulta sociálních věd
Platnost: od 2021
Semestr: zimní
E-Kredity: 6
Způsob provedení zkoušky: zimní s.:
Rozsah, examinace: zimní s.:2/2, Zk [HT]
Počet míst: 76 / 30 (76)
Minimální obsazenost: neomezen
4EU+: ne
Virtuální mobilita / počet míst pro virtuální mobilitu: ne
Stav předmětu: vyučován
Jazyk výuky: angličtina
Způsob výuky: prezenční
Způsob výuky: prezenční
Další informace: http://ies.fsv.cuni.cz/cs/syllab/JEM062
Poznámka: předmět je možno zapsat mimo plán
povolen pro zápis po webu
při zápisu přednost, je-li ve stud. plánu
Garant: PhDr. Jiří Kukačka, Ph.D.
Vyučující: PhDr. Jiří Kukačka, Ph.D.
Hieu Nguyen Thi Hoang, M.A.
Ing. Alena Pavlova
Třída: Courses for incoming students
Anotace -
Poslední úprava: PhDr. Jiří Kukačka, Ph.D. (16.01.2024)
The course covers the fundamentals of econometrics, starting from the statistical foundations. Our primary emphasis will be on the linear regression model and the intuition behind its Ordinary Least Squares (OLS) estimation, an essential method in the toolkit of every economist. We will also explore the theoretical properties of OLS, engage in hypothesis testing, delve into the linear regression model's assumptions, discuss potential violations of these assumptions, and learn appropriate remedies. Furthermore, we will delve into advanced topics, including the issue of endogeneity and logistic regression.

Our main focus will be on the practical applications of econometric techniques. Each topic will be illustrated with empirical examples and reinforced through hands-on exercises during seminars.
Cíl předmětu -
Poslední úprava: PhDr. Jiří Kukačka, Ph.D. (16.01.2024)

The primary objective of this course is to enhance students' understanding of econometrics by establishing, revising, and systematizing their knowledge in this field. This course is highly recommended for students who have completed introductory statistics and seek to learn the fundamental principles of quantitative empirical analysis in economics and finance. Econometrics is essential for understanding the relationships between economic variables, serving as the vital link that connects economic theories with real-world data.

Econometrics equips you with the necessary skills and tools to succeed in your interests, whether they are in economic forecasting, the careful empirical testing of scientific hypotheses from various fields, the accurate estimation of numerical relationships between economic variables to inform policymakers or academic audiences, or just satisfying your natural curiosity.

Literatura -
Poslední úprava: PhDr. Jiří Kukačka, Ph.D. (02.02.2024)

Core textbooks (selected chapters):
   Studenmund, A. H. (2016). Using Econometrics: A Practical Guide. Pearson Education, 7th Ed., .pdf (e-book 7th Global Ed. from 2017 or 6th International Ed. from 2014 also possible).
   Wooldridge, J. M. (2016). Introductory Econometrics: A Modern Approach. Cengage Learning, 6th Ed., .pdf (5th Ed. from 2012 or 7th Ed. from 2018, e-book available, also possible).

Compare editions and pagination in Textbooks_contents.pdf.

Vstupní požadavky -
Poslední úprava: PhDr. Jiří Kukačka, Ph.D. (02.02.2024)

Knowledge of basic statistical concepts is expected and will be recapitulated during the first lecture. A brief overview can be found in the Studenmund textbook (2016, [2014]), Chapter 17 [15]: Statistical Principles, see the 'Files' section. A more advanced summary can be found in the Wooldridge textbook (2016), Appendices B and C-1 to C-3. A useful introduction to statistics (Harvard University) is available on youtube.

Knowledge of basic matrix algebra is also expected and important for understanding the content of some lectures and exercises. A summary of matrix algebra can be found in the Wooldridge textbook (2016), Appendix D. Useful matrix tutorials with exercises to practice and solutions can be found, e.g., here or at Khan Academy.

 
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