Statistical methods for economics

Duration

12h Th, 12h Pr

Number of credits

Lecturer

Yves Brostaux

Language(s) of instruction

French language

Organisation and examination

Teaching in the second semester

Units courses prerequisite and corequisite

Prerequisite or corequisite units are presented within each program

Learning unit contents

Descriptive statistics
- mesures of central location, dispersion, concentration
- index numbers
Time series
- decomposition methods
- smoothing methods
- ARIMA models

Learning outcomes of the learning unit

To learn statistical tools particularly useful to economics. After completing the course the student is expected to - summarize data from the field of business and social sciences, - calculate index numbers, - analyse univariate time series by various approches.

Prerequisite knowledge and skills

Basic skills in applied statistics, for example :

• STAT2004 & STAT2005- Applied statistics (1st & 2nd part)

• INFO2037-1- Introduction to computer science

• HULG0149-1- Office automation

Planned learning activities and teaching methods

• Lectures and Q&A
• Exercises
• Personal work

Mode of delivery (face-to-face ; distance-learning)

Blended learning (face to face and e-learning) and flipped classrooms

Recommended or required readings

- Syllabus and learning material on eCampus (mandatory) - DAGNELIE P. [2007]. Statistique théorique et appliquée. Tome 1 : statistique descriptive et bases de l'inférence statistique. Bruxelles De Boeck, 511 p. (recommanded)

Assessment methods and criteria

Oral examination (20%) Personal reports (80%)

Work placement(s)

Organizational remarks

Lectures : 8 h
Apart from the first introductory session, face-to-face sessions will be devoted to the presentation of the work to be done and questions and answers related to the theory and exercices. These sessions will be prepared beforehand on the basis of documents provided on eCampus, reading carefully the documents and resolving provided exercises .
These sessions will be devoted to an active discussion based on preparations and WILL NOT BE ex cathedra presentations of the material. To make the best of these face-to-face sessions, it is important to prepare them carefully BEFORE the class, which changes the paradigm of traditional lecture course.
Personal work
Three individual graded assignments will be given, one for each section of the course (Statistical parameters, Index numbers and time series), and will be transmitted via the eCampus platform. The instructions related to the completion of this work will be available in due time on eCampus and will be explained at face-to-face sessions.
In addition, ad hoc evaluations can be organized during the face-to-face sessions.
Organization
The detailed face sessions and the work schedule is available in the Planning section of the course of eCampus.
Participation in all educational activities organized is mandatory.

Contacts

Yves Brostaux (Senior lecturer) 081 62 24 69 y.brostaux@ulg.ac.be

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