Introduction to Quantitative Economics

The details
Colchester Campus
Full Year
Undergraduate: Level 4
Thursday 08 October 2020
Friday 02 July 2021
24 August 2020


Requisites for this module
MA108, MA114


EC252, MA108, MA114

Key module for

BA LL12 Economics and Politics,
BA LL13 Economics and Politics (Including Placement Year),
BA LL1F Economics and Politics (Including Year Abroad),
BSC LL14 Economics and Politics (Including Foundation Year),
BSC LL2F Economics and Politics,
BSC LL3F Economics and Politics (Including Year Abroad),
BSC LL4F Economics and Politics (Including Placement Year),
BSC LG01 Economics with Computing,
BSC LG02 Economics with Computing (Including Year Abroad),
BSC LG03 Economics with Computing (Including Placement Year),
BA LN10 Business Economics,
BA LN11 Business Economics (Including Year Abroad),
BA LN12 Business Economics (Including Placement Year),
BA LX10 Business Economics (Including Foundation Year),
BA C841 Economics with Psychology,
BA C851 Economics with Psychology (Including Year Abroad),
BA C861 Economics with Psychology (Including Placement Year),
BSC C148 Economics with Psychology,
BSC C158 Economics with Psychology (Including Year Abroad),
BSC C168 Economics with Psychology (Including Placement Year)

Module description

The course introduces students to the methods of quantitative economics, i.e. to how data are used in economics. Beginning from an elementary level (assuming no background in statistics), the course shows how economic data can be described and analysed.

The elements of probability and random variables are introduced in the context of economic applications. The probability theory enables an introduction to elementary statistical inference: parameter estimation, confidence intervals and hypothesis tests.

With these foundations, students are then introduced to the linear regression model that forms a starting point for econometrics. Throughout the course the emphasis is on the practical application of economic analysis from an empirical perspective.

Module aims

The main objective of this module is to enhance students' knowledge and conceptual understanding of the treatment of data in economics.

Module learning outcomes

Upon completion of the course, each student will be aware of the main sources of economic data; how to construct and interpret graphs of the data; how to construct summary statistics. Students will have acquired a knowledge of the statistical methods needed for the analysis of economic issues. In particular, students' will have learned how to interpret the estimates of simple economic models and to conduct tests of hypotheses about the model's parameters.

The module provides the students with a wide set of tools which will prove to be particularly relevant during their working careers. Their ability to read, understand and properly manipulate data will be strongly enhanced, from both a theoretical and empirical point of view. Students are introduced to the knowledge of widely used statistical computational packages and plenty of examples from real datasets will reinforce student's control of the most important statistical techniques.

Module information

First year students with a background in mathematics may substitute EC114 for the more advanced modules MA114 and MA108 offered by the department of Mathematical Sciences with permission from the department.

Learning and teaching methods

2 lectures per week, weeks 2-11, 16-25. 1 class per week, weeks 3-11, 16-25, 30. Support class available weeks 3-11 and 16-25. Feedback for this module will occur through class meetings where we will go over the answers to problem sets and where you will be able to ask questions about your own method of solution; answers that will be posted on the website for the module that will give you written guidance on the appropriate method to approach the problems, assignments, and tests; and office hours where any additional questions can be addressed. You should be sure that you use these methods to understand how to improve your own performance. For modules including a term paper, the term paper will be returned with individualised feedback that addresses what the marking criteria are and how you could improve your own work.


This module does not appear to have any essential texts. To see non-essential items, please refer to the module's reading list.

Assessment items, weightings and deadlines

Coursework / exam Description Deadline Weighting
Coursework   Autumn Term Test - Timetabled for Monday 18 November 2019 - Week 8    50% 
Coursework   Spring Term Assignment    50% 
Exam  180 minutes during Summer (Main Period) (Main) 

Overall assessment

Coursework Exam
50% 50%


Coursework Exam
50% 50%
Module supervisor and teaching staff
Dr Quentin Lippmann, email:
Prof Eric Smith, email:
Quentin Lippmann and Eric Smith
For further information, please send an email to



External examiner

No external examiner information available for this module.
Available via Moodle
Of 285 hours, 257 (90.2%) hours available to students:
28 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).


Further information

* Please note: due to differing publication schedules, items marked with an asterisk (*) base their information upon the previous academic year.

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