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2011/2 Provisional Module Catalogue - UNDER CONSTRUCTION & SUBJECT TO CHANGE
 Module Code: ECO2010 Module Title: INTERMEDIATE ECONOMETRICS
Module Provider: Economics Short Name: EC208B
Level: HE2 Module Co-ordinator: DRINKWATER SJ Dr (Economics)
Number of credits: 15 Number of ECTS credits: 7.5
 
Assessment Pattern
Unit(s) of Assessment 

Weighting Towards Module 
Mark (%)
2 hour Examination

70
Short multiple choice assessment

10
Computer based test

20

Qualifying Condition(s)
A weighted aggregate mark of 40% is required to pass this module
Module Overview
This module follows on from Introduction to Econometrics and cements the knowledge of multivariate OLS regression that was obtained here. This module extends the analysis into models of time series relationships with a focus on differenct examples of the endogeneity problem and their solutions
Prerequisites/Co-requisites
None
Module Aims
The primary aim of this course is to introduce students to the techiques relevant for the estimation of econometric time-series models. An important emphasis of the course is to provide students with 'hands-on' experience of econometric analysis through using a variety of economic data ssets. For this purpose, a number of datasets will be made available to undertake econometric analysis variables and techniques for panel data will be covered
Learning Outcomes

At the end of the course students will be able to:-

  • interpret econometric models with a variety of functional forms including those with lagged independent and dependent variables
  • appreciate the additional conditions needed to estimate models with time series data
  • understand a number of cncepts relating to OLS estimation with time series data
  • appreciate the problems caused by autocorrelation, diagnose and treat it
  • understand various forms of the endogeneity problem and the solutions that can be used to overcome it. This includes instrumental variables and panel data
  • apply econometric techniques using E-views and interpret the output obtained
Module Content

Topics to be covered

  • introduction to time series methods
  • distributed lag modeles
  • autocorrelation
  • lag dependant variable models
  • non-stationary time series
  • simultaneous equations and instrumental variables
  • introduction to panel data methods
Methods of Teaching/Learning
Lectures (11) and Lab sessions (9)
Last Updated

10 March 2011