INTERMEDIATE ECONOMETRICS - 2023/4

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

Economics

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

Module Availability

Semester 2

Prerequisites / Co-requisites

None

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

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 is worth 30% of the final grade.


• Final exam is worth 70% of the final grade.

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

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:

• 2 hour lecture per week x 11 weeks


• 1 hour lab session per week x 10 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

https://readinglists.surrey.ac.uk
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. 

Employability

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

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.