BPE_INEC Introduction to Econometrics

Faculty of Economics and Administration
Autumn 2024
Extent and Intensity
1/2/0. 6 credit(s). Type of Completion: zk (examination).
In-person direct teaching
Teacher(s)
Thi Hoang Hieu Nguyen (lecturer)
Thi Hoang Hieu Nguyen (seminar tutor)
Guaranteed by
doc. Ing. Daniel Němec, Ph.D.
Department of Economics – Faculty of Economics and Administration
Contact Person: Mgr. Jarmila Šveňhová
Supplier department: Department of Economics – Faculty of Economics and Administration
Timetable
Fri 9:00–9:50 VT105, except Fri 20. 9., except Fri 8. 11.
  • Timetable of Seminar Groups:
BPE_INEC/01: Fri 10:00–11:50 VT105, except Fri 20. 9., except Fri 8. 11., T. Nguyen
Prerequisites
(! BPE_AIEC Introduction to Econometrics ) && (!NOWANY( BPE_AIEC Introduction to Econometrics ))
elementary probability and mathematical statistics
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 24 student(s).
Current registration and enrolment status: enrolled: 24/24, only registered: 2/24, only registered with preference (fields directly associated with the programme): 1/24
fields of study / plans the course is directly associated with
Course objectives
The course is designed to give students experience of using econometric methods important in economics, finance and other business subjects. It provides skills in regression essential for understanding much of the literature of economics, finance, and empirical studies in other areas of business.
We begin with the simple regression and multiple regression models. They are treated in depth and in range of applications. Careful attention is given to the interpretations of regression results and hypothesis testing. A part of the course introduces various modern tools for analyzing models with binary dependent variables.
By the end of the course students should be able to use regression models in many different applications, and to critically examine reported regression results in empirical research in economics and other business studies. They will be able to identify and deal with a number of econometric problems in the analysis of time series and cross-section data, and will have experience of a range of basic econometric methods.
Learning outcomes
The course is designed to give students an understanding of why econometrics is necessary and to provide them with a working knowledge of basic econometric tools so that:
They can apply these tools to modeling, estimation, inference, and forecasting in the context of real world economic problems.
They can evaluate critically the results and conclusions from others who use basic econometric tools.
They have a foundation and understanding for further study of econometrics.
They have an appreciation of the range of more advanced techniques that exists and that may be covered in later econometric courses.
Syllabus
  • 1. Introduction to Econometrics and Working with Data (The Nature of Econometrics and Economic Data)
  • 2. The Simple Regression Model (Linear Regression Model and Ordinary Least Squares Estimator)
  • 3. Multiple Regression Model: Estimation (Motivation, Interpretation, OLS Estimator Properties)
  • 4. Multiple Regression Model: Inference (Hypothesis Testing, Testing Multiple Linear Restricions, Reporting Regression Results)
  • 5. Multiple Regression Model: OLS Asymptotics (Consistency and Large Sample Inference)
  • 6. Multiple Regression Model: Further Issues (More on Functional Form, Goodness-of-Fit, Slection of Regressors, Prediction and Residual Analysis)
  • 7. Multiple Regression Analsysis with Qualitative Information (Using Dummy Variables, The Linear Probability Model)
  • 8. Heteroskedasticity (Robust Inference, Testing for Heteroskedasticity)
  • 9. More on Specification and Data Issues (Functional Form Misspecification)
  • 10. Introduction to Limited Dependent Variable Models (Logit and Probit Models)
Literature
    required literature
  • WOOLDRIDGE, Jeffrey M. Introductory econometrics : a modern approach. Seventh edition. Boston: Cengage Learning, 2020, xxi, 826. ISBN 9781337558860. info
    recommended literature
  • HEISS, Florian. Using R for introductory econometrics. 2nd edition. Düsseldorf: Florian Heiss, 2020, 368 stran. ISBN 9788648424364. info
  • HEISS, Florian and Daniel BRUNNER. Using Python for introductory econometrics. 1st edition. Düsseldorf: Florian Heiss, 2020, 418 stran. ISBN 9788648436763. info
  • KOOP, Gary. Introduction to econometrics. Chichester: John Wiley & Sons, 2008, 371 s. ISBN 9780470032701. info
  • HILL, R. Carter, William E. GRIFFITHS and Guay C. LIM. Principles of econometrics. Fifth edition. Hoboken: Wiley Custom, 2018, xxvi, 878. ISBN 9781119510567. info
Teaching methods
tutorials, class discussion, computer labs practices, drills
Assessment methods
homework assignments, written exam
Language of instruction
English
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
Listed among pre-requisites of other courses
Teacher's information
Any copying, recording or leaking tests, use of unauthorized tools, aids and communication devices, or other disruptions of objectivity of exams (credit tests) will be considered non-compliance with the conditions for course completion as well as a severe violation of the study rules. Consequently, the teacher will finish the exam (credit test) by awarding grade "F" in the Information System, and the Dean will initiate disciplinary proceedings that may result in study termination Students in this course are expected to adhere to the Masaryk University’s high standards of integrity as spelled out in the Disciplinary Code for Students and Directive N.3/2008. Anyone who cheats on tests or exams, will be subject to the penalties set forth in the Code.
The course is also listed under the following terms Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, Autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, Autumn 2021, Autumn 2022, Autumn 2023, Spring 2024.
  • Enrolment Statistics (recent)
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