Econometrics I

Course content

Econometrics I covers the fundamentals of multiple linear regression analysis with cross-sectional data. The course begins by introducing the regression model and the method of Ordinary Least Squares (OLS). Statistical properties such as unbiasedness, consistency and asymptotic normality of the OLS estimator are derived and discussed in detail. Procedures for testing hypotheses regarding population parameters are presented, as well as tests of misspecification. Advanced topics such as Instrumental Variables (IV) estimation and panel data methods are incorporated into the course as supplementary tools to estimate causal relationships.   

The course emphasizes the application and implementation of the presented statistical techniques. Therefore, course participants will have the opportunity to work with real-world data and apply the statistical methods themselves to answer relevant economic questions.  Classes are an important and integral part of the course, where the students will work with real data sets to get hands-on experience with empirical analyses and application of the econometric techniques taught in the course.


Bacheloruddannelsen i økonomi – Obligatorisk fag på 4. semester

The Danish BSc programme in Economics - mandatory course at the 4th semester

Learning outcome

After having completed the course the student should have acquired the following knowledge, skills, and competencies.


  • Fundamentals of multiple linear regression analysis.

  • Assumptions and properties of the OLS estimator, including the necessary and sufficient conditions for unbiased, consistent and efficient estimation.

  • Methods for conducting statistical inference (t-test, F-test, LM-test and Wald-test) conduct t-test, F-tests, LM-tests, and Wald-tests are conducted when the error term is homoscedastic and heteroskedastic.

  • Assumptions and properties of the IV estimator, including how to test for overidentifying restrictions and exogeneity.

  • Assumptions and properties of simple linear panel data estimators (Differences-in-differences, first differences, fixed effect and random effect estimators).

  • Methods for conducting simulation experiments, illustrating the properties of the estimators presented in the course.


  • Be able to conduct a descriptive analysis of a new data set with the aim of being able to apply regression analysis.

  • Be able to derive simple estimators and develop proofs to show that they are unbiasedness, consistent and efficient.

  • Apply the following estimators: ordinary least squares (OLS), weighted least squares (WLS), generalized least squares (GLS) ,instrumental variables (IV), differences-in-differences, first differences (FD), fixed effect (FE) and random effect (RE)

  • To calculate t-test, F-tests, LM-tests, and Wald-tests of restrictions on the coefficients in a linear regression model in the cases where errors are homoscedastic and heteroskedastic.

  • Give an account for the interpretation of coefficients on various types of variables (continuous variables, dummy variables, transformed variables) in a regression model.

  • Conduct tests for misspecification (heteroskedasticity, functional form) and give an account for their interpretation.

  • Apply simple linear panel data estimators on real data sets.

  • Apply simulation experiments to characterize and test the properties of the estimators ad test statistics introduced in the course


  • Be able to assess whether the assumptions underlying the OLS estimator are satisfied. This includes being able to assess in a particular application whether the regressors in a regression model are likely to be exogenous, and if not, what the source of endogeneity may be.

  • Be able to assess whether the regressors in a regression model applied to a real data set are likely to be endogenous. and conduct instrumental variables estimation while providing a precise account of assumptions and interpretations.

  • Be able to assess when it is relevant to apply simple linear panel data estimators in real applications and be able to implement such analyses.

  • Be able to use econometric reasoning to choose among different sets of parameter estimates.

  • Report estimation results and give an account for their interpretation.

  • Be able to apply a given set of parameter estimates to make an assessment of the economic consequences of the object of the analysis.


The syllabus will be presented at the first lecture. In classes the students will work with assignments and be asked to work with problems using real-world data, but also to solve theoretical problems and to conduct simulation experiments. Statistical software for applying the techniques introduced in the course will be introduced and the students will apply the software for answering assignments. In the classes the students will also practice written presentation of an econometric analysis.

Introductory Econometrics: A Modern Approach, 6th Edition, Jeffrey M. Wooldridge Michigan State University ISBN-10: 130527010X, ISBN-13: 9781305270107 912 Pages, 2016

Lecture Notes:

  • Simulation Experiments in Econometrics (Jørgensen, 2015)

  • Instrumental Variables Estimation (Leth-Petersen, 2016)

Participants are expected to have knowledge about basic statistical methods and probability theory corresponding to the syllabus of 'Probability theory and statistics' and use of the mathematical tools, including matrix algebra, introduced in Mathematics A and B.

2x2-hour lectures each week from week 36 to 50 (except week 42).
3 hours of exercise classes each week from week 36/37 to 50 (except week 42).

Skema for BA kan ses på https:/​/​​polit_ba/​undervisning/​Lektionsplan-E17/​skemaer/​Sider/​default.aspx

Timetable and venue:
To see the time and location of lectures and exercise classes please press the link/links under "Se skema" (See schedule) at the right side of this page (E means Autumn, F means Spring). The lectures is shown in each link.

You can find the similar information partly in English at
-Select Department: “2200-Økonomisk Institut” (and wait for respond)
-Select Module:: "2200-E17; [Name of course]” or “2200-F18; [Name of course]”
-Select Report Type: “List – Weekdays”
-Select Period: "Efterår/​Autumn - Week 30-5" or “Forår/Spring – Week 5-30”
Press: “ View Timetable”

Please be aware regarding exercise classes:
- The schedule of the exercise classes is only a pre-planned schedule and can be changed until just before the teaching begins without the participants accept. If this happens it will be informed in KUnet or can be seen in the app myUCPH and at the above link.
- That the study administration allocates the students to the exercise classes according to the principles stated in the KUnet.
- It is not possible to change class after the second registration period has expired.
- If there is not enough registered students or available teachers the exercise classes may be jointed.
- The student is not allowed to participate in an exercise class not registered, because the room has only seats for the amount of registered student.
- The teacher of the exercise class cannot correct assignments from other students than the registered students in the exercise class except with group work across the classes. .

Timetable and venue for Spring 2018 will be available from 7th of November 2017.

7,5 ECTS
Type of assessment
Written assignment, 12 hours
take-home exam.The exam assignment is given in English and can be answered in English or in Danish. Language must be chosen at the course or exam registration.
From spring 2018 the exam must be answered in English every semester.
Student can work in groups consisting of maximally three members.
All aids allowed
Marking scale
7-point grading scale
Censorship form
External censorship
if chosen by the Head of Studies.
Criteria for exam assessment

Students are assessed on the extent to which they master the learning outcome for the course.

To receive the top grade, the student must be able to demonstrate in an excellent manner that he or she has acquired and can make use of the knowledge, skills and competencies listed in the learning outcomes.

Single subject courses (day)

  • Category
  • Hours
  • Lectures
  • 56
  • Class Exercises
  • 42
  • Preparation
  • 96
  • Exam
  • 12
  • English
  • 206