Syllabus

Title
4824 Econometrics I
Instructors
Dr. Carolina Bernardita Lennon Zaninovic
Contact details
  • Type
    PI
  • Weekly hours
    2
  • Language of instruction
    Englisch
Registration
02/17/22 to 02/23/22
Registration via LPIS
Notes to the course
Subject(s) Bachelor Programs
Dates
Day Date Time Room
Wednesday 03/16/22 01:00 PM - 03:00 PM D1.1.074
Wednesday 03/23/22 01:00 PM - 03:00 PM D1.1.074
Wednesday 03/30/22 01:00 PM - 03:00 PM D1.1.074
Wednesday 04/06/22 01:00 PM - 03:00 PM D2.0.374
Wednesday 04/20/22 01:00 PM - 03:00 PM D1.1.074
Wednesday 04/27/22 01:00 PM - 03:00 PM D5.1.004
Wednesday 05/04/22 01:00 PM - 03:00 PM D1.1.074
Wednesday 05/11/22 01:00 PM - 03:00 PM D1.1.074
Wednesday 05/18/22 01:00 PM - 03:00 PM D1.1.074
Wednesday 05/25/22 01:00 PM - 03:00 PM D2.0.326
Wednesday 06/01/22 01:00 PM - 03:00 PM D2.0.038
Wednesday 06/08/22 01:00 PM - 03:00 PM D1.1.074
Wednesday 06/15/22 01:00 PM - 03:00 PM D2.0.038
Wednesday 06/22/22 01:00 PM - 03:00 PM D1.1.074

Contents

The econometrics program is offered in a cycle over 3 terms:  Econometrics I,   Econometrics II, and Applied Econometrics.

In Econometrics I, we will cover the foundations of econometrics: causality and correlation, the Ordinary Least Squares (OLS) method, OLS model assumptions and properties, functional forms, dummy variables, and heteroscedasticity. The course includes as well an introduction of a statistical software (R, Stata or Python) in order to conduct empirical exercises.

Learning outcomes

This course provides an introduction to the analysis of economic data using econometric methods. After having taken the course, students should be able to understand empirical studies published in scientific journals and carry out econometric work by themselves.

Attendance requirements

Notice: the course will start on the 16 March 2022.

Attendance is compulsory and up to two sessions are allowed to be missed without formal justification.

 

Teaching/learning method(s)

Lectures, exercises

We will go through chapters 1 to 8 of Wooldridge book “Introductory Econometrics. A Modern Approach”

Introduction (CHAPTER 1)

Simple regression model (CHAPTER  2)

 Multiple regression model (CHAPTER  3)

 Small Sample Inference (CHAPTER  4)

 Functional Form (CHAPTER  6)

 Binary Variables (CHAPTER  7)

 Heteroskedasticity (CHAPTER  8)

 

Depending on time, we will also cover  CHAPTER 5 on "OLS Asymptotics"

Assessment

Continuous assessments ( 30 points),  final exam (40 points), homework (30 points). At least 35 points together in the ontinuous assessments and final exam in order to pass the course

Prerequisites for participation and waiting lists

If you have a valid registration for the lecture, but will not participate, please deregister during the registration period of LPIS. Your place will be available for other students.

During the registration period, free places are filled according to the “first-come, first-served” principle. After the end of the registration period, the number of places is increased and students on the waiting list will be registered for the lecture. Students in the BBE-program will be added first, should places remain, they will be filled by BaWiSo-students based on their progress in their studies.

Attendance in the first session is necessary, any absence will lead to deregistration! Any remaining places in the classes will be allocated to students attending the first session according to the waiting list. No places will be allocated by email or by phone.

Registration for the lecture cannot be guaranteed. Any student dropping out of the course who has already submitted a gradable task will receive a negative grade.

Readings

1 Author: Wooldridge, J.
Title:

Introductory Econometrics. A Modern Approach


Publisher: Mason, Ohio, South-Western
Edition: 5th, 6th or 7th edition
Remarks: It is expected that the students read the corresponding chapters before each lecture.
Year: 2012
Content relevant for class examination: Yes
Recommendation: Essential reading for all students
Type: Book

Recommended previous knowledge and skills

Sound knowledge of statistics (distributions, moments, and properties) and mathematics  (optimization, derivatives, sum operators and matrix).

 

Availability of lecturer(s)

carolina.lennon@wu.ac.at

Other

Software:

Carrying out empirical work is part of the content of the course. As standard software package we will use R, but, if you you prefer, you can use Stata or Python instead.

Supplementary Literature:

Baltagi, B. (2008). Econometrics, New York: Springer.

Greene, W. (2003). Econometric analysis, 5. ed., U.S.River, N.J.: Prentice Hall.

Gujarati, D.N., Porter, D.C. (2009). Basic Econometrics, New York: McGraw Hill.

Hackl, P. (2005). Einführung in die Ökonometrie. München: Pearson Studium.

 

 

Last edited: 2022-06-15



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