Econometrics 2: Multiple Regression and Applications ECON4004
- Academic Session: 2022-23
- School: Adam Smith Business School
- Credits: 15
- Level: Level 4 (SCQF level 10)
- Typically Offered: Semester 2
- Available to Visiting Students: Yes
In this course we build further on what was taught in Econometrics 1 in the use of statistical methods to analyse data. In addition to multivariate OLS models, we introduce relevant techniques such as instrumental variables, panel data, time series and non-linear models.
Lectures: Tuesday 14.00-16.00 (10 x 2 hours)
1-hour revision lecture to be scheduled outwith normal teaching hours.
Labs: 10 hours (5 x 2 hour)
Tutorials: 9 hours (9 x 1 hour)
Labs and Tutorials are held at various times and can be selected on MyCampus
ECON4003: Econometrics 1: Introduction to Econometrics
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.
The main aim of this course is to help students develop a clear and complete understanding of various econometric techniques that should be used to analyse different economic, financial, political and social datasets. The emphasis throughout this course is on empowering the student to thoroughly understand the most fundamental econometric ideas and tools, and how this knowledge is of practical relevance to professionals in business, industry, government, and academia.
Intended Learning Outcomes of Course
By the end of this course, students should be able to:
1. Critically analyse linear and non-linear econometric models
2. Identify, estimate and interpret models using panel data and time series data
3. Assess the validity of statistical assumptions underpinning a model
4. Make effective use of information technology to retrieve and analyse economic data by constructing econometrically robust models
5. Work effectively in teams to collaborate and achieve common goals in retrieval, analysis and presentation of information
6. Communicate clearly and effectively the results of econometric analysis of data.
Minimum Requirement for Award of Credits