Research Methods and Introductory Econometrics

Module code: ECN219

The module provides an introduction to econometrics which concentrates on the understanding of basic concepts through explanation and example.

Aims of the module

In introducing econometrics through the understanding of basic concepts, the module aims to:

  • develop students' skills in the use of econometric techniques
  • enable students to grasp the essentials of regression output to allow them to access journal articles

Learning objectives

At the end of this module, typical students will be able to:

  • conduct standard hypothesis tests
  • demonstrate knowledge of regression analysis OLS
  • distinguish between different functional forms and justify which are appropriate for estimating economic models
  • interpret regression output – specifically, what coefficients represent
  • appraise the problems associated with using OLS when classical assumptions are violated
  • understand the concepts of instrumental variable analysis and when such an approach is relevant
  • appreciate the relevance of omitted variables in modelling
  • use the econometric software STATA to undertake empirical analysis

Syllabus

Part I – The Classical Linear Regression Model (CLRM)

Simple regression – the Classical Linear Regression Model; multiple regression analysis; hypothesis testing; interpreting regression results; dummy variable uses in econometrics

Part II – Beyond the CLRM

Omitted variables; non-spherical disturbances – heteroscedasticity; non-spherical disturbances – autocorrelation; instrumental variables; simultaneous equations

Teaching methods

Lectures, tutorials and computer practicals

Assessment

An unseen end-of-module examination (75%) and one piece of coursework (25%)

Basic reading

A detailed reading list will be distributed at the start of the course

Prerequisites ECN130 and ECN120. Only available to BA single and dual economics students

Restrictions Cannot be taken with ECN216, ECN308 or ECN309

Module leader  To be confirmed 

Semester Autumn

Credits 20