Note from June 29, 2020
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
5 credits
30.0 h + 15.0 h
Q1
Teacher(s)
Beine Michel (compensates Dejemeppe Muriel); Dejemeppe Muriel;
Language
French
Prerequisites
The prerequisite(s) for this Teaching Unit (Unité d’enseignement – UE) for the programmes/courses that offer this Teaching Unit are specified at the end of this sheet.
Main themes
The course covers the basic instruments of econometric analysis at an intermediate (for subjects introduced in previous courses) or introductory level (for new subjects). Examples of how these methods are applied to management problems are given. An important aspect of the course is learning econometric modelling: students are taught how to take a theoretical, abstract and general relation between variables and apply it to the formulation and estimation of a particular concrete form that relation might take in a given context. They will also be introduced to econometric software during the course.
Aims
At the end of this learning unit, the student is able to : | |
| 1 | This course is intended to give students a background in the theory and practice of Econometrics. The emphasis is on understanding the methods and their relevance to the solution of management problems. By the end of the course, students should be able to use these methods for simple question solving and to interpret the results of an econometric analysis while being aware of the limitations of the methods. |
The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.
Content
Regression analysis with cross-sectional data
Chapter 1. General introduction
Chapter 2. The simple regression model
Chapter 3. Multiple Regression Analysis: Estimation
Chapter 4. Multiple Regression Analysis: Inference
Chapter 5. Multiple Regression Analysis: OLS Asymptotics
Chapter 6. Multiple Regression Analysis: Advanced issues
Chapter 7. Multiple Regression Analysis with qualitative information
Chapter 8. Multiple Regression Analysis: Heteroscedasticity
Chapter 9. Multiple Regression Analysis: Specification and data issues
+ Introduction to the statistical software STATA
Chapter 1. General introduction
Chapter 2. The simple regression model
Chapter 3. Multiple Regression Analysis: Estimation
Chapter 4. Multiple Regression Analysis: Inference
Chapter 5. Multiple Regression Analysis: OLS Asymptotics
Chapter 6. Multiple Regression Analysis: Advanced issues
Chapter 7. Multiple Regression Analysis with qualitative information
Chapter 8. Multiple Regression Analysis: Heteroscedasticity
Chapter 9. Multiple Regression Analysis: Specification and data issues
+ Introduction to the statistical software STATA
Teaching methods
The non-French speaking students are expected to learn the course content by themselves based on the reference book in English (see below). For these students, two sessions of practical work with STATA (in English) are organized in a computer room during the quarter. Students are invited to participate to an introductory session (in English) to the STATA software at the beginning of the quarter.
Evaluation methods
The exam consists in a written exam in English on 20 points. The exam can be taken in January 2020 and/or August 2020.
Other information
Prerequisites:
1) Mathematics in economics and management
2) Statistics in economocmis and management
1) Mathematics in economics and management
2) Statistics in economocmis and management
Online resources
See Moodle UCL (http://moodleucl.uclouvain.be/).
Bibliography
Livres de référence (Reference books):
Jeffrey Wooldridge (2016), Introductory Econometrics: A Modern Approach, 6th Edition, Cengage Learning.
Jeffrey Wooldridge (2016), Introductory Econometrics: A Modern Approach, 6th Edition, Cengage Learning.
Jeffrey Wooldridge (2016) Introduction à l’économétrie: Une approche moderne, traduction de la 5ième édition américaine, de Boeck.
Faculty or entity
ESPO
Programmes / formations proposant cette unité d'enseignement (UE)
Title of the programme
Sigle
Credits
Prerequisites
Aims
Master [120] in Agricultural Bioengineering
Master [120] in Agriculture and Bio-industries
Minor in Economics
Mineure en statistique et science des données