2020/2021 KANCMECV1702U Cross Section and Panel Econometrics
English Title  
Cross Section and Panel Econometrics 
Course information 

Language  English 
Course ECTS  7.5 ECTS 
Type  Elective 
Level  Full Degree Master 
Duration  One Semester 
Start time of the course  Autumn 
Timetable  Course schedule will be posted at calendar.cbs.dk 
Max. participants  80 
Study board 
Study Board for HA/cand.merc. i erhvervsøkonomi og matematik,
MSc

Course coordinator  


Main academic disciplines  


Teaching methods  


Last updated on 01072020 
Relevant links 
Learning objectives  


Course prerequisites  
The course has a high technical level. Students
are expected to have knowledge of the statistical properties of
ordinary least squares estimation and maximum likelihood
estimation, as well as hypotheses tests about parameters in
regression analysis.
Knowledge of matrix algebra, fundamentals of probability and mathematical statistics are required. Basic knowledge of either R or STATA. 

Examination  


Course content, structure and pedagogical approach  
Topics in Cross Section Econometrics: We consider various violations of common model assumptions (such as GaussMarkov assumptions), including heteroskedasticity, auto/serial correlation, omitted variables, functional form misspecification, measurement error and simultaneity. We see how this can be tested for and what solutions exist (e.g. robust inference statistics, GLS, IV/2SLS methods). We then consider crafting and estimation of a system of equations including simultaneous equations and seemingly unrelated regression by 2SLS/3SLS and System GMM.
Topics in Panel Models: We start with the main static linear panel models: Pooled OLS, FE,RE,FD. We then combine common static linear panel models with IV methods (FDIV, FEIV and RE IV, HausmanTaylor type models). We consider dynamic panel models (AndersonHsiao/ System 2SLS, ArrelanoBond/ SGMM) before we move to nonlinear panel models (binary dependent variable only). Here we extend ML estimation to cope with dependent observations (using KullbackLeibler Information criterion) and consider methods to correct estimated standard errors and statistics. The final point will be to consider panel attrition/unbalanced panels, if time permits. 

Description of the teaching methods  
The course comprises of 25 hours of lectures and 8 hours of computer classes. The first computer class is an introductory class. The following classes cover problem sets.  
Feedback during the teaching period  
1) Office hours.
2) Computer classes: students are encouraged to present their solutions to problem sets to receive formative feedback. 

Student workload  


Expected literature  
Lectures:
Further recommended readings, revision material and articles will be posted on Canvas.
Textbooks:
Croissant and Millo (Wiley, 2018) "Panel Data Econometrics with R". 