Econometrics I
Course content
Econometrics I covers the fundamentals of multiple linear regression analysis with crosssectional 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 realworld 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 handson experience with empirical analyses and application of the econometric techniques taught in the course.
BSc programme in Economics  mandatory
After having completed the course the student should have acquired the following knowledge, skills, and competencies.
Knowledge

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 (ttest, Ftest, LMtest and Waldtest) conduct ttest, Ftests, LMtests, and Waldtests 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 (Differencesindifferences, first differences, fixed effect and random effect estimators).

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

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), differencesindifferences, first differences (FD), fixed effect (FE) and random effect (RE)

To calculate ttest, Ftests, LMtests, and Waldtests 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
Compentencies

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 realworld 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 ISBN10: 130527010X, ISBN13: 9781305270107 912 Pages, 2016
Lecture Notes:

Simulation Experiments in Econometrics (Jørgensen, 2015)

Instrumental Variables Estimation (LethPetersen, 2016)
Participants are expected to have knowledge about basic statistical methods and probability theory corresponding to the syllabus of 'Probability theory and statistics' (Econometrics A) and use of the mathematical tools, including matrix algebra, introduced in Mathematics A and B.
Schedule:
Each week 2x2hour lectures and 3 hours of classes for 14 weeks.
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 (16E means Autumn 2016,
F17 means Spring 2017). The lectures is shown in each link.
You can find the similar information partly in English at
https://skema.ku.dk/ku1617/uk/module.htm
Select Department: “2200Økonomisk Institut” (and wait for
respond)
Select Module:: “2200E16; [Name of course]” or “2200F17; [Name
of course]”
Select Report Type: List
Select Period: “Efterår/Autumn – Weeks 303” or “Forår/Spring –
Week 429”
Press: “ View Timetable”
Please be aware regarding exercise classes:
 The schedule of the exercise classes is only a preplanned
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.
 If too many students have wished a specific class, students will
be registered randomly at another class.
 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.
 That all exercise classes will be taught in
English.
for enrolled students. More information about registration, schedule, rules, courses etc. can be found at the student intranet (KUnet) for courses (BA Danish).
For enkelfagsstuderende sker tilmelding via Åbent Universitet og Merit.
 ECTS
 7,5 ECTS
 Type of assessment

Written assignment, 12 hourstakehome exam.
The exam assignment is given in English and can be answered in English or in Danish. Language must be chosen at the course registration.
Student can work in groups consisting of maximally three members.  Aid
 All aids allowed
 Marking scale
 7point grading scale
 Censorship form
 External censorship
20 % external censorship
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
Kursusinformation
 Language
 English
 Course number
 AØKB08020U
 ECTS
 7,5 ECTS
 Programme level
 Bachelor
 Duration

1 semester
 Price
 Schedulegroup

Teaching:
Autumn: Week 3650
Spring: Uge 621
Timeschedule: See "Remarks"
Exam and resits: See "Exam"  Studyboard
 Department of Economics, Study Council
 Department of Economics
Course responsibles
 Rasmus Jørgensen
(168170827c84823d797e8176747d82747d4f74727e7d3d7a843d737a)
Spring  Søren LethPetersen
(197773766972327069786c3174697869767769724469677372326f7932686f)
Autumn
Teacher
Lectures: See ‘Course responsibles’
Teachers of exercise classes:
Autumn 2016:
Ex. Class 1: Kristoffer Gamst.
Ex. Class 2: Rasmus Bjørn.
Both classes will be taught in English
Spring 2017:
Ex. class 2: Magnus Tolum Buus
Ex. class 3: Tobias Kofoed Kjær
Ex. class 4: Marie Gram Pietraszek
Ex. class 5: Thomas Woergaard Kjær
Ex. class 6: Rasmus Bjørn
Ex. class 8: Anders Muldbjerg Kruse
Ex. class 9: Anne Toft HansenThomas
Ex. class 10: Anita Eskesen
(ex.class 1 and 7 don´t exist)
Be aware that the schedule of the estimated preplanned exercise
classes can be changed up to the start of the semester. All classes
will be taught in English
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