ADVANCED QUANTITATIVE METHODS IN ECONOMICS - 2023/4
Module code: ECOM077
The first part of the module is designed to provide the necessary foundation in mathematical and statistical techniques for the study of economics at graduate level. The second part provides an introduction to programming using specialist programming software. Students will learn how to use numerical methods in the context of mathematic optimisation and data analysis.
LAZOPOULOS Ioannis (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: 16
Independent Learning Hours: 90
Lecture Hours: 12
Tutorial Hours: 6
Guided Learning: 11
Captured Content: 15
Prerequisites / Co-requisites
The module encompasses salient aspects of the basic algebra, probability theory, calculus and optimisation, as well as data clearing and analysis, code parallelization and web scraping.
|Assessment type||Unit of assessment||Weighting|
|School-timetabled exam/test||CLASS TEST (1 HR)||10|
|Coursework||Take Home Problem Set 1||10|
|Coursework||Take Home Problem Set 2||10|
|Coursework||Take Home Problem Set 3||30|
|Examination||FINAL EXAMINATION (60 MIN)||40|
The assessment strategy is designed to provide students with the opportunity to demonstrate that they have achieved the modules learning outcomes and, by association, developed their digital capabilities, global intelligence, resourcefulness, and employability, among other module attributes.
Thus, the summative assessment for this module consists of:
A class test worth 10% of the final grade
Two pieces of coursework, each worth 10% of the final grade
A final piece of coursework worth 30% of the final grade
A final exam worth 40% of the final grade
Formative assessment and feedback Feedback is provided in the form of verbal feedback through discussions of open-ended questions during lectures or questions provided in problem sets during tutorials. Students also receive detailed solutions to questions/exercises against which they can compare their answers to enhance their understanding. Solutions to the class test and coursework are released at the end of the assessment which includes feedback on student performance and advice on how to improve where appropriate.
- The module aims to equip students with the essential quantitative skills required for studying economics at post-graduate level in economics along with programming skills in data analysis which are imperative for any career path in Economics or Finance.
|001||Evidence the capacity to solve quantitative problems in under time-constraint||CKPT|
|002||Engage with the management of datasets and apply mathematical and statistical analysis tools in a practical context||CKPT|
|003||Demonstrate effective work in a small, international team towards a shared goal||CKPT|
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 ensure that students achieve the module¿s learning outcomes. Students have access to pre-recorded videos (11 hours) summarising the learning outcomes of the week and highlighting important concepts ahead of the weekly in-person sessions. These sessions comprise of lectures (22 hours) which are recorded and released, and tutorials over the first 6 weeks (6 hours) which are not recorded. Lectures emphasise the understanding of theory, whereas workshops are designed to be interactive involving discussion in solving various problem sets. An additional component involving weekly guided learning (11 hours) is included.
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.
Upon accessing the reading list, please search for the module using the module code: ECOM077
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: Digital Capabilities Through learning outcome 2 Resourcefulness and Resilience Through learning outcome 2 Employability Through learning outcome 3 Global and Cultural Intelligence Through learning outcome 3
Programmes this module appears in
|Economics (Econometrics and Big Data) MSc||1||Compulsory||A weighted aggregate mark of 50% is required to pass the module|
|Economics (Policy Evaluation and Data Analysis) MSc||1||Compulsory||A weighted aggregate mark of 50% is required to pass the module|
|Economics (Macroeconomics and Financial Markets) MSc||1||Compulsory||A weighted aggregate mark of 50% is required to pass the module|
|Economics (International Economics) MSc||1||Compulsory||A weighted aggregate mark of 50% is required to pass the module|
|Economics MSc||1||Compulsory||A weighted aggregate mark of 50% is required to pass the module|
|Financial Data Science MSc||1||Optional||A weighted aggregate mark of 40% 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.