Econometrics (Bologna) STATS4067
- Academic Session: 2024-25
- School: School of Mathematics and Statistics
- Credits: 12
- Level: Level 4 (SCQF level 10)
- Typically Offered: Semester 2
- Available to Visiting Students: No
- Collaborative Online International Learning: No
Short Description
The course provides the basics of econometric modelling.
Timetable
Timetable information is available from the University of Bologna.
http://corsi.unibo.it/1Cycle/StatisticalSciences/Pages/course-timetable.aspx?CodiceCorso=8873&Indirizzo=A32&AnnoCorso=2
Requirements of Entry
This course is only available to students on the Double Degree programme in Statistics with the University of Bologna.
Excluded Courses
-/-
Co-requisites
-/-
Assessment
End-of-course examination, carried out in accordance with the assessment procedures and regulations of the University of Bologna.
Main Assessment In: April/May
Are reassessment opportunities available for all summative assessments? Not applicable for Honours courses
Reassessments are normally available for all courses, except those which contribute to the Honours classification. For non-Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below.
Course Aims
This course aims
■ to introduce students to econometric modelling;
■ to develop a solid understanding of single-equation econometric models; and
■ to expose students to the problem of endogenous regressors and how it can be overcome.
Intended Learning Outcomes of Course
By the end of the course students will be able to:
■ specify and estimate linear, single-equation econometric models;
■ address the issue of endogenous regressors; and
■ perform a specification analysis of the model.
Minimum Requirement for Award of Credits
Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.