2024/2025 BABMECV1031U 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  


Main academic disciplines  


Teaching methods  


Last updated on 11102024 
Relevant links 
Learning objectives  


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 "KANCMECV1249U Panel Econometrics" og "KANCOECO1056U 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 “BABHAAI1108U Introduction to Econometrics with R”. These skills are elementary knowledge of probability calculus, mathematical statistics and matrix algebra, equivalent to Appendices AD in Wooldridge (2020) and basic working knowledge of R. 

Examination  
The exam in the subject consists of two parts:


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 nonlinear models
A Multiple regression analysis
A0 Intro: What is econometrics? (1h) A1 Estimation by OLS, Properties, GaussMarkov, 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 midterm exam or to obtain advice on individual academic development questions. 

Student workload  


Expected literature  
A more detailed reading list will be made available at the start of the course. 