View Specification DocumentEconometrics 1: Introduction to Econometrics ECON4003
- Academic Session: 2026-27
- School: Adam Smith Business School
- Credits: 15
- Level: Level 4 (SCQF level 10)
- Typically Offered: Semester 1
- Available to Visiting Students: Yes
- Collaborative Online International Learning: No
- Curriculum For Life: No
Short Description
This course intends to revise statistical material required for regression analysis and introduce students to linear regression models, estimation, hypothesis testing, prediction and inferencing.
Timetable
Lectures: 10 x 2 hours.
An additional 2-hour revision lecture outwith normal teaching hours.
9 x 1-hour tutorials
6 x 2-hour computer labs
Requirements of Entry
University of Glasgow students must have achieved Subject Honours entry requirements as detailed below to enrol on this course. Permission is required for non-Economics students to take this course as an outside option.
A GPA of 12 (average C3) in Economics 2A and 2B with no course grade below D3, attained at the first attempt. A minimum grade of D3 in both ECON1012, Introductory Mathematics for Economists and ECON1013, Introductory Statistics for Economists unless exemptions were agreed on the basis of other courses taken in year 1. Refer to course specifications for details. These grades may be achieved at the second attempt.
Excluded Courses
None
Assessment
1. Practical skills assessment; Individual; 800 words; 30%; ILOs 1-5.
2. Degree exam: in-person; Individual; 120 minutes; 70%; ILOs 1-4.
Main Assessment In: December
Course Aims
The main aims of this course are to build upon the foundation provided by the statistics component in second year by providing an introduction to the fundamental theoretical concepts and applications of statistics as they relate to the linear regression model. To this end, we will discuss random variables; probability distributions of random variables (e.g. normal, chi-square, t and F); the multi-variate regression model; desirable properties of an estimator (e.g. unbiasedness, consistency, and efficiency); the Gauss-Markov conditions; and inferences in multiple regression model (e.g. model specification, collinearity and heteroscedasticity).
Intended Learning Outcomes of Course
By the end of this course, students should be able to:
1. critically analyse linear regression models
2. identify and assess the properties of ordinary least squares estimators under different model assumptions
3. estimate linear regression models and make inference
4. communicate clearly and effectively the results of econometric analysis
5. use statistical software to perform econometric analysis on empirical data