EC5062 - Principles of Econometrics (2026/27)
| Module specification | Module approved to run in 2026/27 | ||||||||||
| Module title | Principles of Econometrics | ||||||||||
| Module level | Intermediate (05) | ||||||||||
| Credit rating for module | 15 | ||||||||||
| School | Guildhall School of Business and Law | ||||||||||
| Total study hours | 195 | ||||||||||
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| Running in 2026/27(Please note that module timeslots are subject to change) |
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Module summary
This is a core quantitative module for a range of undergraduate courses in economics, finance and banking. It provides a foundation in statistical methods and focuses on econometric analysis. It examines the theory and application of the Classical Linear Regression Model (CLRM), providing a firm grounding in the theory of Ordinary Least Squares (OLS) and an appreciation of its limitations.
The aims of the module are:
1. You will develop an understanding of statistical methods and the Classical Linear Regression Model (CLRM) and its application to economics and finance problems.
2. You will examine the causes and consequences of violation of the assumptions of the Classical Linear Regression Model.
3. You will acquire knowledge and skills to use statistical/econometric software packages such as Excel /Eviews to analyse findings to economics and finance problems.
4. You will develop a range of transferable and subject specific skills such as: data and quantitative analysis; problem solving; IT; written; research; and evaluation.
Prior learning requirements
None
Available for Study Abroad? YES
Syllabus
Review of statistics: LO1 and LO4
Probability distributions, sampling theory, estimation, confidence intervals, hypothesis testing and applications to economic, finance and banking.
Correlation analysis, hypothesis testing and applications to economics and finance.
Introduction to econometrics: LO2 and LO4
The Classical Linear Regression Model: assumptions, specification, estimation, hypothesis-testing and forecasting.
Violations of the assumptions of the Classical Linear Regression Model: LO2 and LO4
Causes and consequences of violation of the assumptions leading to regression problems.
Functional forms and dummy variables.
Use of econometric software: LO3 and LO4
Access sources of relevant economic and financial information
Produce, interpret and explain the output of dedicated econometric software (e.g. EViews) and solve simple and multiple linear regression problems.
Balance of independent study and scheduled teaching activity
Learning consists of ‘formal’ classroom learning directed by the teaching team, and reflective independent learning. The formal learning involves lectures, seminars and computer-based exercises that may use software such as Excel/ EViews, while the independent learning includes reading of the course material, working on weekly exercises and preparing the assessment.
The module is delivered in a three-hour session each week which comprises a two-hour lecture, and a one-hour seminar. The two-hour lecture introduces students to key concepts. Seminars and IT workshops enable students to gain a deeper understanding of the key concepts and present their solutions to the problems set and raise questions on the lecture material. The seminars and IT workshops provide opportunities for empirical analysis, active and reflective learning, and formative feedback.
The module makes extensive use of a virtual learning environment platform, WebLearn, where module handbook, lecture slides, lecture recordings, seminar questions, guideline answers to seminar questions, IT exercises, assessment and feedback strategies, assessment and grading criteria, EViews videos and other relevant learning materials are provided.
All learning and teaching activities provide students with knowledge and understanding of statistics and econometrics. The weekly exercises give students diverse skills as working independently, problem solving, writing concisely and clearly, retrieving secondary data from various online sources and interpreting and analysing them using econometric software.
Transferable skills are developed in lectures and seminars, and through independent directed learning and assessment. Skills development is enhanced through working cooperatively solving economic and finance problems.
Initiative and independence are developed progressively through the module such that students are encouraged to take greater responsibility for their learning.
Students are encouraged to reflect on their learning.
Learning outcomes
On successful completion of this module students will be able to:
1. Demonstrate a broad knowledge and a systematic understanding of statistics; correlation analysis; hypothesis testing and application to economics, finance and banking.
2. Develop an in-depth understanding of the Classical Linear Regression Model (CLRM), the assumptions; causes and consequences of violation of assumptions; hypothesis testing of regression coefficients; and application of the model to economics and finance problems.
3. Analyse and interpret data and explain multiple regression results using econometric software.
4. Demonstrate a range of transferable skills: data and quantitative analysis; problem solving; IT; written; research; analytical thinking; and evaluation of empirical work within economics, finance and banking.