ECONOMETRICS 1 - 2023/4

Module Overview

This module is an introduction to the methods of specification, estimation and testing of econometric models in a general multivariate setting. The techniques are applied to real data making use of the econometric packages.

Module provider

Economics

Module Leader

SANTOS SILVA Joao (Economics)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

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

Module Availability

Semester 1

Prerequisites / Co-requisites

None

Module content

Indicative content includes:

• Multiple Regression analysis using cross sectional data


• Asymptotic properties of OLS


• Regression analysis using qualitative information


• Functional form


• Autocorrelation


• Heteroskedasticity


• Instrumental variables estimation


• Econometric models with time series

Assessment pattern

Alternative Assessment

NA

Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate their ability to understand and carry out econometric techniques. In particular, assessment features real-word problem-based tasks, and as such it aims at improving resourcefulness and resilience of students.

This module has a technical and a practical component. As such, assessments put emphasis on both aspects in the form of a midterm and a final exam, in which students are asked to analyse real economic and financial data.

Thus, the summative assessment for this module consists of:

• class test


• final examination

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 midterm feedback is provided for all individual questions.

Module aims

• Provide the student with the theoretical and practical skills necessary to construct state of the art, single and multi-equation econometric models.
• The module will equip the student with the ability to critically assess empirical work in economics, with a view to enabling the student to use econometrics to catalogue and describe empirical regularities and test various propositions.

Learning outcomes

Methods of Teaching / Learning

The learning and teaching strategy is designed to improve digital capabilities, employability, and resourcefulness and resilience through full use of statistical software, and real world examples and datasets.This prepares students for the study of economics and econometrics at FHEQ Level 7 (first week); gives students the theoretical tools they need to go out and analyse real world situations; and encourage hands-on study of empirical problems.

The learning and teaching methods include:

• Readings using lecturers guidance


• Solving exercises


• Responding to questions in class


• Preparing and taking part in the test


• Lectures


• Labs

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: ECOM042

Other information

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

Full use of statistical software in both teaching, especially in lab classes and learning activities, and assessment.

Employability

Real-world examples and dataset throughout lectures, learning activities and assessment, statistical software.

Resourcefulness and Resilience

Assessment features real-world problem-based tasks that are designed to enhance student resourcefulness. 

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.