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2024/2025  BA-BMECV1031U  Econometrics

English Title
Econometrics

Course information
Language English
Course ECTS 7.5 ECTS
Type Elective
Level Bachelor
Duration One Semester
Start time of the course Autumn
Timetable Course schedule will be posted at calendar.cbs.dk
Max. participants 40
Study board
Study Board for HA/cand.merc. i erhvervsøkonomi og matematik, BSc
Course coordinator
  • Ralf Andreas Wilke - Department of Economics (ECON)
Main academic disciplines
  • Finance
  • Statistics and quantitative methods
  • Economics
Teaching methods
  • Face-to-face teaching
Last updated on 25-01-2024

Relevant links
Learning objectives
  • Understand econometric estimation and inference methods for higher dimensional economic and financial data.
  • Understand how to model, estimate and interpret the partial or causal relationship between two variables in economic or finance models with many variables.
  • Understand the relevance of assumptions on the econometric model for the properties of estimation and inference results.
  • Appropriately choose an econometric model from those introduced in the course for economics or finance applications and assess its suitability.
  • Understand estimation results and interpret them in the context of economic and financial applications.
  • Relate R-code and R-output to the econometric models introduced in the course.
  • Conduct econometric analysis in R.
Course prerequisites
Knowledge of mathematics, statistics and probability calculus as acquired during the first two years of the HA(mat) programme.

Basic working knowledge of the statistical software R is required.

The course is an element of the econometrics progression line. It prepares the students for more specialised econometrics courses such as "KAN-CMECV1249U Panel Econometrics" og "KAN-COECO1056U Financial Econometrics”.

Note: Students of other study lines than HA(mat) such as HA alm., do not meet the prerequisites for this course unless they have acquired additional mathematical and statistical skills through suitable elective courses, such as “BA-BHAAI1108U Introduction to Econometrics with R”. These skills are elementary knowledge of probability calculus, mathematical statistics and matrix algebra, equivalent to Appendices A-D in Wooldridge (2020) and basic working knowledge of R.
Examination
The exam in the subject consists of two parts:
Del 1 - Økonometri:
Sub exam weight30%
Examination formWritten sit-in exam on CBS' computers
Individual or group examIndividual exam
Assignment typeWritten assignment
Duration2 hours
Grading scale7-point grading scale
Examiner(s)One internal examiner
Exam periodAutumn
AidsLimited aids, see the list below:
The student is allowed to bring
  • USB key for uploading of notes, books and compendiums in a non-executable format (no applications, application fragments, IT tools etc.)
  • Any calculator
  • In Paper format: Books (including translation dictionaries), compendiums and notes
The student will have access to
  • Advanced IT application package
Make-up exam/re-exam
Same examination form as the ordinary exam
The number of registered candidates for the make-up examination/re-take examination may warrant that it most appropriately be held as an oral examination. The programme office will inform the students if the make-up examination/re-take examination instead is held as an oral examination including a second examiner or external examiner.
Description of the exam procedure

The assignment will be about a given problem and data. The project must show that the student understands the theory, including the statistical model, the econometric approach and its assumptions, can do empirical analysis with the statistical software R by choosing suitable estimation and inference approaches, and applying them correctly. It must also demonstrate that the student can extract interpretable results and can interpret them correctly. All learning objectives of the course are relevant for this partial exam.

 

Each sub examination must be passed with at least 02.

Final - Økonometri:
Sub exam weight70%
Examination formWritten sit-in exam on CBS' computers
Individual or group examIndividual exam
Assignment typeWritten assignment
Duration2 hours
Grading scale7-point grading scale
Examiner(s)Internal examiner and external examiner
Exam periodWinter
AidsLimited aids, see the list below:
The student is allowed to bring
  • An approved calculator. Only the models HP10bll+ or Texas BA ll Plus are allowed (both models are non-programmable, financial calculators).
  • Language dictionaries in paper format
The student will have access to
  • Advanced IT application package
Make-up exam/re-exam
Same examination form as the ordinary exam
The number of registered candidates for the make-up examination/re-take examination may warrant that it most appropriately be held as an oral examination. The programme office will inform the students if the make-up examination/re-take examination instead is held as an oral examination including a second examiner or external examiner.
Description of the exam procedure

 

The final exam covers all parts of the course (multiple regression model, endogeneity, non-linear models).

 

All but one learning objectives are relevant for this partial exam. The student will not conduct econometric analysis in R in the final exam.

 

Each sub examination must be passed with at least 02.
Course content, structure and pedagogical approach

High dimensional data sets are increasingly available for the analysis of economic, business, and finance problems. As these data are normally generated by usual business activity or operation, they originate from real business or economic processes that are not compatible with the standard assumptions of statistical regression models. By breaching these assumptions, alternative econometric models need to be employed that can provide valid estimation and inference results in these scenarios and are tailored to uncover the partial or causal relationship between economic variables.

 

The course gives students an understanding of elementary econometric regression models which are often used in economics and finance to analyse data sets. The course introduces the material from both a theoretical and practical angle. It contains formal treatment of statistical assumptions and properties of estimators using matrix notation. It also presents applications with real data from economics and finance, where students learn how to use the statistical software R to obtain interpretable results. Strong emphasis is put on explaining the link between the statistical theory and empirical practice. Students eventually learn how the models and their restrictions translate into practical work with the statistical software R.

 

The course consists of lectures and exercise classes. The lectures are followed by computer classes, where students deepen their understanding by working on theoretical and empirical problems. Students can work in groups to solve the weekly problem sets and present their solutions to obtain feedback.

 

 

The course has two parts:

 

A Multiple regression analysis

B Endogeneity and non-linear models

 

 

 

A Multiple regression analysis

 

 

A0 Intro: What is econometrics? (1h)

A1 Estimation by OLS, Properties, Gauss-Markov, variable choice (5h)

A2 Violations of Gauss Markov- assumptions (heteroskedasticity, serial correlation, (F)GLS) (4h)

A3 Policy analysis (2h)

 

MIDTERM EXAM

 

B Topics in cross section econometrics

 

B1 Endogeneity (4h)

B2 Simultaneous equation models (2h)

B3 Maximum likelihood estimation (2h)

B4 Limited dependent variable models (3h)

B5 Evaluation and review (1h)

 

FINAL EXAM
Description of the teaching methods
Lectures and exercise classes.

The theory is presented during lectures with empirical examples and sample R code. There are weekly problem sets with exercises to deepen the understanding of the theory, to train practical analysis skills including interpretation of results, and to link the theory with practice by working with statistical software.

Students are invited to work in groups on the problem sets and present their solutions during the exercise classes.
Feedback during the teaching period
Office hours:
Students can book 20 minutes slots during weekly office hours to obtain feedback on particular problems with the course material, their mid-term exam or to obtain advice on individual academic development questions.
Student workload
Lectures and exercises 38 hours
Exams (mid-term and final) 4 hours
Preparation 164 hours
Expected literature
  • Lecture notes.

 

  • Wooldridge, J. (2020), Introductory Econometrics, 7th Edition, Cengage. (selected chapters).

 

  • Wooldridge, J. (2010), Econometric Analysis of Cross Section and Panel Data, 2nd Edition, The MIT Press. (selected chapters).

 

 

 A more detailed reading list will be made available at the start of the course.
Last updated on 25-01-2024