Module code: ECOM058

Module Overview

This module focuses on causation and identification using both cross-section and panel data from both experimental and observational sources. The techniques are applied to real data with the use of an econometric package.

Module provider


Module Leader

COSTAS-FERNANDEZ Julian (Economics)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

Module cap (Maximum number of students): N/A

Overall student workload

Workshop Hours: 11

Independent Learning Hours: 95

Lecture Hours: 11

Guided Learning: 22

Captured Content: 11

Module Availability

Semester 2

Prerequisites / Co-requisites


Module content

The module covers suitable content for masters students who may not have prior knowledge or experience in applied econometric techniques. The emphasis is on working with cross-country and panel data using suitable programming software.

Assessment pattern

Assessment type Unit of assessment Weighting
Coursework Coursework Assignment 50

Alternative Assessment


Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate their ability to understand and carry out econometric techniques. This module has a technical and a practical component. Assessment emphasises  work based on econometric and statistical packages in the form of an assignment, in which students are asked to analyse real economic and financial data. The technical component is assessed via a final examination. Students benefit from assessments as these are a chance for students to showcase their level of understanding about the subject both in terms of theoretical notions and capability to apply the theory to real world examples.

Thus, the summative assessment for this module consists of:

  • Coursework Assignment (linked to learning outcomes 1& 2) and

  • Final Examination: (linked to learning outcomes 1-4)

Formative assessment and feedback:

This is done by specific, individualised written comments, feedback meetings with students and general feedback in class.

Module aims

  • This module aims to provide the student with the theoretical and practical skills necessary to construct econometric models, with an emphasis on causation and identification using both cross-section and panel data from both experimental and observational sources. The module will equip the student with the ability to undertake, understand, and critically assess empirical work in economics, with a view to enabling the student to use econometrics to catalogue and describe empirical regularities and test various propositions. This module also equips students with the necessary analytical, statistical and software skills required to successfully undertake the writing of the dissertation within the module ECOM055 Advanced Economics Project.

Learning outcomes

Attributes Developed
001 Understand the principles of estimation and hypothesis testing and be critically aware of the assumptions made in building advanced econometric models KCT EMPLOYABILITY; RESOURCEFULNESS AND RESILIENCE
002 Demonstrate comprehensive knowledge of the properties of different estimators and tests KCT EMPLOYABILITY; RESOURCEFULNESS AND RESILIENCE
003 Show a practical understanding of the application of econometric techniques to actual data using computer packages KCPT DIGITAL CAPABILITIES; EMPLOYABILITY; RESOURCEFULNESS AND RESILIENCE
004 Proficiently use testing and estimation capabilities of software packages, evaluating the relative merits of competing methodologies KCPT DIGITAL CAPABILITIES; EMPLOYABILITY; RESOURCEFULNESS AND RESILIENCE

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Methods of Teaching / Learning

The learning and teaching strategy is designed to:

  • give students the theoretical tools they need to go out and analyse real world situations encourage rigour in their approach to problems

  • encourage hands-on study of empirical problems

The learning and teaching methods include:

  • readings using the lecturer's guidance and solving exercises

  • responding to questions in class and office hours

  • Lectures aimed at exploring and discussing topics of theoretical nature

  • Workshops aimed at working real-world scenarios

  • Guided learning aimed at deepening the understanding of topics and complementing with extra readings the material seen in the lectures

Students are expected to actively engage in workshops and to interact with the lecturer and colleagues. This is to promote an active rather than passive approach to learning.

These methods have been chosen so as to develop an appreciation of real word econometric application and their usefulness in finance and economics. Further, through these methods students will also appreciate the benefits deriving from applying scientific rigorous approaches to solve theoretical as well as applied problems. Furthermore, these methods clearly feedback the applicability of the key pillars as outlined in the learning outcomes. For instance, through the application of rigorous techniques to real world problems students will be expected to develop skills linked to the Employability and Resourcefulness and Resilience pillars.

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list
Upon accessing the reading list, please search for the module using the module code: ECOM058

Other information

In line with the University's curriculum framework, the School of Economics is committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability and Resourcefulness and Resilience. This module is designed to allow students to develop knowledge, skills, and capabilities in the following areas:

Resourcefulness and Resilience

Self-evaluation is built into assessment process, creating necessary space for students to reflect on own performance, whilst reviewing and asking for specific feedback.


Assessment mimics professional scenarios and real word situations experienced by applied economists/econometricians. 

Digital Capabilities

Students will acquire knowledge of important soft skills such as proficiency in the use of econometric software.



Programmes this module appears in

Programme Semester Classification Qualifying conditions
Economics MA 2 Compulsory A weighted aggregate mark of 50% is required to pass the module

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2023/4 academic year.