Module descriptor template: updated Aug 2014

MODULE SPECIFICATION TEMPLATE

MODULE DETAILS

Module title Introduction to Econometrics

Module code EC203

Credit value 20

Level Mark the box to the right of the

appropriate level with an ‘X’

Level 4 Level 5 X Level 6 Level 7 Level 8

Level 0 (for modules at foundation level)

Entry criteria for registration on this module

Pre-requisites Specify in terms of module codes or

equivalent

EC104 Quantitative Methods for Economics and Finance

Co-requisite modules Specify in terms of module codes or

equivalent

Module delivery

Mode of delivery Taught X Distance Placement Online

Other

Pattern of delivery Weekly X Block Other

When module is delivered Semester 1 Semester 2 Throughout year X

Other

Brief description of module

content and/ or aims Overview (max 80 words)

The module will provide students with a basic understanding of

econometric methods and models that will equip students with essential

skills and knowledge required for further study and help to enhance

students’ subsequent employment opportunities in the field of economics.

The module will enable students to deepen their knowledge of regression

analysis so that they can apply simple models appropriately while

investigating financial and economic phenomena using software typically

employed by researchers and analysts.

Module team/ author/

coordinator(s)

Dr Ray Bachan

School BBS

Site/ campus where

delivered

This module will normally be delivered at Moulsecoomb

Course(s) for which module is appropriate and status on that course

Course Status (mandatory/ compulsory/

optional)

BSc (Hons) Economics Compulsory

MODULE AIMS, ASSESSMENT AND SUPPORT

Aims This module aims to:

provide students who have little or no knowledge of econometrics with the opportunity to study some of its basic principles and

Module descriptor template: updated Aug 2014

techniques that will be invaluable for further study and future employment;

provide students with the opportunity to use specialist software to formulate, estimate and diagnose econometric models;

provide students with the opportunity to formally apply economic and financial theories to real world situations; and

introduce students to the ‘language’ of econometrics as an aid to understanding a significant portion of the applied economics and financial literature that uses econometrics.

Learning outcomes On successful completion of the module the student will be able to:

Subject specific:

1. Understand the assumptions that underlie the classical linear regression model and to be aware of implications for the model when there are departures from these assumptions.

2. Apply a variety of statistical testing principles to investigate

departures from the underlying assumptions using an appropriate econometric software package.

3. Understand how to apply time-series analysis to a wide range

of macroeconomic and financial data and to demonstrate good knowledge and understanding of the empirical requirements.

4. Appraise cointegration analysis to identify long run equilibrium

relationships.

5. Construct simple forecasting models and demonstrate an awareness of their shortcomings.

6. Construct and apply appropriate models to investigate asset

price volatility.

7. Construct models using qualitative variables in order to assess changes in government policy and its effect on markets and the environment.

Cognitive: I. Demonstrate skills of problem solving and analysis.

II. Demonstrate an ability to understand the language of standard

econometric textbooks and professional articles.

III. Effectively communicate quantitative and qualitative information, together with analysis and commentary.

Content Inference in the general linear model: t-tests, F-tests. LM-tests and the R2.

Regression model specification tests: RESET Test, J-B Test, Whites Test, and D-W Test.

The consequence of and the remedies for heteroscedasticity, autocorrelation and omitted variables.

Generalised Least Squares and Weighted Least Squares.

Introduction to ARCH and GARCH models and their application to financial and volatile markets.

Module descriptor template: updated Aug 2014

Introduction to Time Series: Stationarity and the Correlogram of residuals.

Testing for Trends and Unit Roots: The Dickey-Fuller and Augmented Dickey-Fuller Tests.

Introduction to Cointegration and the Error Correction Model.

ARIMA Forecasting Models.

Learning support Indicative reading:

The latest editions of:

Asteriou, D. and Hall, S. Applied Econometrics. Palgrave

Dougherty, C. Introduction to Econometrics. Oxford

Gujarati, D.N. Basic Econometrics. McGraw-Hill International

Gujarati, D, N. Econometrics by Example. Palgrave.

Wooldridge, J.M. Introductory Econometrics. Thompson

Selected articles from academic journals such as: Economic Journal,

Journal of Finance, Journal of Money, Credit, and Banking, American

Economic Review, Journal of Monetary Economics, and the Journal of

Economic Perspectives.

Websites: Students will be able to download datasets associated with

the core text.

Software: Eviews/STATA and Excel

There is also a dedicated area on studentcentral where students will be

able to download lecture slides/notes seminar problems and answers,

datasets and simulations and other useful material. Students will also

have opportunity to discuss and communicate their ideas and

knowledge through discussion boards and self-test exercises on

studentcentral.

Teaching and learning activities

Details of teaching and

learning activities

The module content is delivered through a series of lectures and

developed in subsequent seminars/workshops through structured

exercises, practical demonstrations using econometric software, and

discussion.

Lectures: 20 Open Learning: 0

Seminars: 14 Self Study: 100

Workshops: 6 Assessment: 60

Total: 200

Allocation of study hours (indicative) Where 10 credits = 100 learning hours

Study hours

SCHEDULED

This is an indication of the number of hours students can expect to

spend in scheduled teaching activities including lectures, seminars, 40

Module descriptor template: updated Aug 2014

tutorials, project supervision, demonstrations, practical classes and

workshops, supervised time in workshops/ studios, fieldwork, and

external visits.

GUIDED INDEPENDENT

STUDY

All students are expected to undertake guided independent study

which includes wider reading/ practice, follow-up work, the

completion of assessment tasks, and revisions.

160

PLACEMENT

The placement is a specific type of learning away from the University.

It includes work-based learning and study that occurs overseas. 0

TOTAL STUDY HOURS 200

Assessment tasks

Details of assessment on

this module

The assessment criteria will be based on the expected learning

outcomes stated above and will consist of two elements:

1) An individual practical assignment consisting of a maximum of 2,000

words incorporating the use of an appropriate econometric package

and comprising of 50% of the overall marks. Key learning outcomes

assessed: 1, 2, 3, I, II and III

2) A two-hour closed book unseen examination that comprises 50% of

the overall marks. 1-6, I and III.

Types of assessment task1 Indicative list of summative assessment tasks which lead to the award of credit or which are required for

progression.

% weighting (or indicate if

component is

pass/fail)

WRITTEN

Written exam 50%

COURSEWORK

Written assignment/ essay, report, dissertation, portfolio, project

output, set exercise

PRACTICAL

Oral assessment and presentation, practical skills assessment, set

exercise 50%

EXAMINATION INFORMATION

Area examination board Economics, BBS

External examiners

Name Position and institution Date appointed Date tenure

ends

Homagni Choudhury Degree Scheme Coordinator,

Aberystwyth School of

Management and Business

Oct 2016 Sep 2020

1 Set exercises, which assess the application of knowledge or analytical, problem-solving or evaluative skills, are included

under the type of assessment most appropriate to the particular task.

Module descriptor template: updated Aug 2014

QUALITY ASSURANCE

Date of first approval Only complete where this is not the

first version

Date of last revision Only complete where this is not the

first version

Date of approval for this

version

9 Nov 2016. Periodic Review March 2017, editorial August 2017

Version number 1.2

Modules replaced Specify codes of modules for which

this is a replacement

Available as free-standing module? Yes No X