Last update: PhDr. Mgr. Jiří Kukačka, Ph.D. (13.09.2023)
The primary objective of this course is to enhance students' understanding of econometrics by establishing, revising, and systematizing their knowledge in this field. Our main focus will be on the practical applications of econometric techniques. Throughout the semester, our primary emphasis will be on the linear regression model and its Ordinary Least Squares (OLS) estimation—a simple yet powerful method that is essential in the toolkit of every economist.
During the course, we will cover the fundamentals of econometrics, starting from the statistical foundations and progressing through the theory and intuition behind OLS estimation. We will also explore the properties of OLS, engage in hypothesis testing, delve into the assumptions of the linear regression model, 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. To foster a deep understanding of the limitations of regression analysis, we will repeatedly address the concept of causality.
Each topic will be illustrated with practical examples and reinforced through hands-on exercises during seminars.
Last update: PhDr. Mgr. Jiří Kukačka, Ph.D. (13.09.2023)
The primary objective of this course is to enhance students' understanding of econometrics by establishing, revising, and systematizing their knowledge in this field. Our main focus will be on the practical applications of econometric techniques. Throughout the semester, our primary emphasis will be on the linear regression model and its Ordinary Least Squares (OLS) estimation—a simple yet powerful method that is essential in the toolkit of every economist.
During the course, we will cover the fundamentals of econometrics, starting from the statistical foundations and progressing through the theory and intuition behind OLS estimation. We will also explore the properties of OLS, engage in hypothesis testing, delve into the assumptions of the linear regression model, 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. To foster a deep understanding of the limitations of regression analysis, we will repeatedly address the concept of causality.
Each topic will be illustrated with practical examples and reinforced through hands-on exercises during seminars.
Aim of the course -
Last update: PhDr. Mgr. Jiří Kukačka, Ph.D. (13.09.2023)
This course is highly recommended for students who have successfully 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. Its relevance extends across numerous spheres of both professional and personal life.
Econometrics equips you withthe necessary skills andt 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.
Last update: PhDr. Mgr. Jiří Kukačka, Ph.D. (13.09.2023)
This course is highly recommended for students who have successfully 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. Its relevance extends across numerous spheres of both professional and personal life.
Econometrics equips you withthe necessary skills andt 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.
Literature -
Last update: PhDr. Mgr. Jiří Kukačka, Ph.D. (13.09.2023)
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.
Last update: PhDr. Mgr. Jiří Kukačka, Ph.D. (13.09.2023)
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.
Entry requirements -
Last update: PhDr. Mgr. Jiří Kukačka, Ph.D. (13.09.2023)
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.
Last update: PhDr. Mgr. Jiří Kukačka, Ph.D. (13.09.2023)
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.
