APPLIED ECONOMETRICS - 2018/9

Module code: ECOM058

Module provider

Economics

Module Leader

GABRIEL V Dr (Economics)

Number of Credits

15

ECTS Credits

7.5

Framework

FHEQ Level 7

JACs code

L110

Module cap (Maximum number of students)

N/A

Module Availability

Semester 2

Overall student workload

Independent Study Hours: 128

Lecture Hours: 22

Assessment pattern

Assessment type Unit of assessment Weighting
Coursework Coursework test/assignment 50
Examination Examination (1h 30min) 50

Alternative Assessment

n/a

Prerequisites / Co-requisites

MANM280 is a co-requisite for this module

Module overview

This module is an introduction to the methods of specification, estimation and testing of econometric models in a general multivariate setting, with an emphasis on time series and panel data. The techniques are applied to real data making use of the econometric packages EViews and Stata.

Module aims

This module aims to provide the student with the theoretical and practical skills necessary to construct state of the art, single and multi-equation econometric models, with an emphasis on time series and panel data. The module will 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 econometrics to catalogue and describe empirical regularities and test various propositions.

Learning outcomes

Attributes Developed
001 Systematically understand the principles of estimation and hypothesis testing in a multivariate setting CKT
002 Demonstrate comprehensive knowledge of the properties of different estimators and tests CKT
003 Demonstrate a practical understanding of the application of econometric techniques to actual data using computer packages CKPT
004 Be critically aware of the assumptions made in building econometric models CKT
005 Proficiently use the time series and panel data testing and estimation capabilities of a range of packages, evaluating the relative merits of competing methodologies CKPT

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Module content

Indicative content includes: 


Multiple Regression analysis 
Functional form 
Autocorrelation 
Econometric models with time series  
Panel data methods

Methods of Teaching / Learning

The learning and teaching strategy is designed to:


give students the theoretical tools they need to go out and analyse real world situations 
encourage rigour in their approach to problems
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/assignment
2-hour lecture per week x 11 weeks

Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate their ability to understand and carry out econometric techniques. This module has a technical and a practical component. Assessment emphasises work based on econometric and statistical packages in the form of an assignment, in which students are asked to analyse real economic and financial data. The technical component is assessed via a final examination. Thus, the summative assessment for this module consists of: 50% coursework assignment and 50% Examination: 1 hour and 30 minutes Formative assessment and feedback: This is done by specific, individualised written comments, feedback meetings with students and general feedback in classes.

Reading list

Reading list for APPLIED ECONOMETRICS : http://aspire.surrey.ac.uk/modules/ecom058

Other information

None

Programmes this module appears in

Programme Semester Classification Qualifying conditions
Economics MA 2 Compulsory A weighted aggregate mark of 50% 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 2018/9 academic year.