2013/2014 KANCM_N63 Methods in Empirical Business Economics
English Title  
Methods in Empirical Business Economics 
Course information 

Language  English 
Exam ECTS  7.5 ECTS 
Type  Elective 
Level  Full Degree Master 
Duration  One Quarter 
Course period  Autumn, Second Quarter
Changes in course schedule may occur Wednesday 09.5011.30, week 4451 Thursday 09.5011.30, week 4451 
Time Table  Please see course schedule at eCampus 
Max. participants  60 
Study board 
Study Board for MSc in Economics and Business
Administration

Course coordinator  


Administration: Ida Lyngby  il.eco@cbs.dk  
Main academic disciplines  


Last updated on 05042013 
Learning objectives  
• Demonstrate an ability to detect
situations in which the traditional linear model is not adequate
and be able to explain why.
• Demonstrate an understanding of the approaches introduced in the course • Undertake a fairly simple econometric analysis using models within at least one of the main topics of the course. This implies being able to:


Course prerequisites  
While the course does not explicitly build on the “Applied Econometrics” course, basic knowledge of the linear regression model (OLS) is required.  
Examination  


Course content and structure  
This course provides an opportunity for students to gain direct
experience estimating a simple model from economics or finance. The
course will introduce three widely used approaches in econometrics
that extend skills learned in the basic course in Applied
Econometrics at the Applied Economics and Finance cand.merc. line.
Students will be required to perform an analysis that uses one or
more of the techniques of the course. This course will be very
useful to students who plan to undertake empirical analysis as part
of their Masters Thesis.
The course will begin with a brief summary of basic econometric
methods with a focus on practical issues such as data quality,
recoding raw data and treatment of missing data. We will also
review the important classical assumptions, emphasizing when
empirical results can be interpreted as causal effects or
correlations between variables.
The course will cover the following topics:
1) Binary outcomes– Outcomes that are of particular interest within corporate governance, finance and many other fields are often binary in nature. For example, one might want to study the factors that affect whether a firm will make a public offering. In this section, students will learn the different approaches to estimating models with binary outcomes: linear probability models, probit and logit models. 2) Program/policy evaluation using differenceindifference estimation– Differenceindifference (DD) estimators are a relatively easy way to estimate the causal impact of a program or policy on any outcome of interest. For example, one might estimate the effect of minimum wages on unemployment, whether free trade agreements affect stock market prices, or how antitrust laws impact cartel behaviour? 3) Trending time series and cointegration –These methods are
very relevant for students who plan to write their thesis within
finance or more generally those who plan to use time series data.
In this section of the course, students will learn how to work with
data that has a time trend and how to test whether two time series
are cointegrated. The methods learned in this section can be used
to test various important hypotheses from economic and finance
models.
Students will:


Teaching methods  
The course will combine lectures with demonstrations and instruction in computer labs.  
Expected literature  
Main text book:
Stock, J.H. and Mark W. Watson. Introduction to
Econometrics, 3rd edition, ISBN 9780201715958
Relevant articles will be selected from each of the three main topics. 