Undergraduate 

Economics MA(SocSci)/BAcc/BSc/MA

Econometrics 1: Introduction to Econometrics ECON4003

  • Academic Session: 2021-22
  • School: Adam Smith Business School
  • Credits: 15
  • Level: Level 4 (SCQF level 10)
  • Typically Offered: Semester 1
  • Available to Visiting Students: Yes
  • Available to Erasmus Students: Yes

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; one additional 1-hour revision lecture out with normal teaching hours

9 hours tutor-led problem-solving sessions (9 x 1 hour)

9 hours tutor-led computing sessions (6 x 1.5 hour) 

Excluded Courses

None

Assessment

ILO

Main Assessment In: December

Are reassessment opportunities available for all summative assessments? Not applicable

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

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

Minimum Requirement for Award of Credits

None.