Module code: ECO2010

Module Overview

This module follows on from Introductory Econometrics and considers econometric theory and methods when Gauss Markov assumptions fail to hold. The first half introduces different examples of the endogeneity problem and their solutions. The second half of this module deals with stationary and nonstationary time series. 

Module provider


Module Leader

GOLSON Eric (Economics)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 5

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

Overall student workload

Independent Learning Hours: 53

Lecture Hours: 22

Laboratory Hours: 10

Guided Learning: 43

Captured Content: 22

Module Availability

Semester 2

Prerequisites / Co-requisites


Module content

Indicative content includes:

  • Instrumental variables, two stage least squares, local average treatment effect 

  • Simultaneous equations 

  • Introduction to time series methods, dynamic models, autocorrelation 

  • Non-stationary time series, cointegration and error-correction models 

Assessment pattern

Assessment type Unit of assessment Weighting
Online Scheduled Summative Class Test Online Test within a 4hr window 30
Examination EXAMINATION 70

Alternative Assessment

Not applicable

Assessment Strategy

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

Their understanding of basic econometric methods, and ability to apply these techniques to analyse time series data and linear models with endogeneity that may arise from omitted variables, unobserved heterogeneity or simultaneous equations.

Thus, the summative assessment for this module consists of:

  • Midterm assessment.

  • Final exam.

Formative assessment and feedback

Students receive verbal feedback during lectures and tutorials through direct questioning (in which multiple questions and real-world examples of the use of economics are discussed). In addition to this, they receive guideline solutions to tutorial questions, against which they can compare their own results. After the test feedback is provided for all individual questions.

Module aims

  • This module aims to Introduce students to the techniques relevant for the estimation (i) of econometric time-series models and (ii) in the presence of endogenous variables.
  • Introduce students to the techniques relevant for the estimation (i) in the presence of endogenous variables and (ii) of econometric time-series models. An important emphasis of the course is to give students with 'hands-on' learning experience of econometric analysis using a variety of economic data sets along side the theory. For this purpose, a number of datasets will be made available to undertake econometric analysis.

Learning outcomes

Attributes Developed
001 Understand a number of concepts relating to OLS and instrumental variables estimation. KCPT
002 Understand various forms of the endogeneity problem and the solutions that can be used to overcome it. This includes methods involving instrumental variables and estimation of simultaneous equations. KCPT
003 Interpret econometric models with a variety of functional forms including those with lagged independent and dependent variables. KCPT
004 Apply econometric techniques using some software package and interpret the output obtained. KCPT

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 analysing economic data in more realistic situations where Gauss Markov assumptions do not hold

  • Appreciate the complexities of econometric analysis, understanding importance and intuition behind various estimation strategies and tests

The learning and teaching methods include:

  • Lectures

  • Lab sessions

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: ECO2010

Other information

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 particularly in the following areas:

Digital capabilities

Students will develop the capacity to manage information and databases pertaining to various types of data. The module also requires students to use statistical software ot showcase familiarity with econometric techniques.  These activities have the ultimate effect of leveraging students' digital skills. 


Students are equipped with theoretical and practical problem-solving skills, and transferable mathematical and theoretical knowledge that will allow them to analyze in theory and in practice data driven financial and economic applications. All of this is highly valuable to employers for different graduate roles. 

Global and cultural capabilities

Real life examples will be utilized in this module which will build students global capabilities. This module allows students to develop skills which will allow them to analyze data and collaborate effectively from individuals around the world. 

Resourcefulness and Resilience

Key event sessions, normalize feelings in class dialogue and discussion, design ‘Service Learning’-based assessment or module activities; module designed with a solution-focused, independent learning approach.

Programmes this module appears in

Programme Semester Classification Qualifying conditions
Business Economics and Data Analytics BSc (Hons) 2 Compulsory A weighted aggregate mark of 40% is required to pass the module
Business Economics BSc (Hons) 2 Compulsory A weighted aggregate mark of 40% is required to pass the module
Economics and Finance BSc (Hons) 2 Compulsory A weighted aggregate mark of 40% is required to pass the module
Economics BSc (Hons) 2 Compulsory 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 2025/6 academic year.