Surrey University Stag


Module code: ECO3010

Module Overview

This module builds on the econometrics foundation from the Introductory Econometrics and Intermediate Econometrics courses. The emphasis of this module is to introduce econometric techniques used to analyse microeconomic data. The first half of this module considers techniques to achieve causal inference. The second half studies maximum likelihood estimation of limited dependent variables models (logit, probit, Poisson, censoring, and selectivity), as well as basic machine learning methods.

Module provider


Module Leader

SAHA Nirman (Economics)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 6

JACs code: L140

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

Overall student workload

Independent Learning Hours: 84

Lecture Hours: 22

Guided Learning: 11

Captured Content: 33

Module Availability

Semester 2

Prerequisites / Co-requisites


Module content

The course covers models used in the policy evaluation literature (RCTs, regression with covariates, difference-in-differences and regression discontinuity design), maximum likelihood estimation of limited dependent variables models (logit, probit, Poisson, censoring, truncation and selectivity) and basic machine learning methods.

Assessment pattern

Assessment type Unit of assessment Weighting
Online Scheduled Summative Class Test MIDTERM TEST 30
Examination Online FINAL EXAMINATION 70

Alternative Assessment

Not applicable

Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate:

Their understanding of econometric methods beyond simple linear regression framework that are commonly used in analysing microeconomic data, and the ability to use relevant computer packages to investigate real world economic problems.

Thus, the summative assessment for this module consists of:

  • Mid-term computer-based class test, worth 30% of the final mark.

  • Final written exam containing questions covering all 11 weeks. Worth 70% of the final mark. The exam has two sections. Section A contains four questions for students to choose three. Section B contains three questions for students to choose two. The allocation of questions will take into account that the materials in the first 5 weeks have partially been examined in the mid-term exam.

Formative assessment and feedback

Students receive verbal feedback during lectures through direct questioning (in which multiple questions and real-world examples of the use of economics are discussed). There are also homework assignments throughout the course, where feedback is provided for all individual questions.In addition to this, they receive guidance and illustrations to the use of Stata.

Module aims

  • 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 micro-econometrics to catalogue and describe empirical regularities and test various propositions.

Learning outcomes

Attributes Developed
001 Use Stata to analyse microeconomic datasets KCPT
002 To understand models used for causal inference including regression, randomised control trials, difference in differences and regression discontinuity designs. KCP
003 To be able to apply the knowledge gained in (2) to a variety of real world research contexts. KPT
004 To gain familiarity with modelling limited dependent variables, in particular models for corner solutions, counts, and binary outcomes. Understand the concepts of censoring, corner solutions, truncation, sample selection, and their relationship. KCPT
005 Gaining a basic understanding of machine learning methods. PT

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:

  • Develop skills in modelling economic problems empirically and use computer packages to estimate and test various propositions

  • Appreciate the intuition behind different econometric methods applied in different situations (theory and practice)

The learning and teaching methods include:

  • 1 hour workshop per week plus one hour captured content x 6 weeks 

  • 2 hour lecture / lab per week x 5 weeks

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: ECO3010

Programmes this module appears in

Programme Semester Classification Qualifying conditions
Economics and Finance BSc (Hons) 2 Optional A weighted aggregate mark of 40% is required to pass the module
Economics BSc (Hons) 2 Optional A weighted aggregate mark of 40% is required to pass the module
Economics and Mathematics BSc (Hons) 2 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 2021/2 academic year.