[skip to content]

Centre for Financial and Management Studies (CeFiMS)

Econometric Principles & Data Analysis

Course Code:
Unit value:


This course provides an introduction to econometric methods, examining how we can start from relationships suggested by economic theory, formulate those relationships in mathematical and statistical models, estimate those models using sample data, and make statements based on the parameters of the estimated models.

The course examines the assumptions that are necessary for the estimators to have desirable properties, and the assumptions necessary for us to make statistical inference based on estimated models. The course also explores what happens when these assumptions are not satisfied and what to do in these circumstances.

You are provided with Eviews econometric software as part of the course. We recommend that you take this course before progressing onto the more advanced sequel 'Econometric Analysis & Applications'.


Study Guide

You will receive a looseleaf binder containing eight 'course units'. The units are carefully structured to provide the main teaching, defining and exploring the main concepts and issues, locating these within current debate and introducing and linking the further assigned readings. The unit files are also available to download from the Online Study Centre.


Damodar N. Gujarati and Dawn C. Porter (2010) Essentials of Econometrics, 4th Edition,(International edition), New York: McGraw-Hill

Econometric software

You will receive the software package for university econometrics courses, EViews 6, designed for Windows users.

Online Study Centre

You will have access to the OSC, which is a web-accessed learning environment. Via the OSC, you can communicate with your assigned academic tutor, administrators and other students on the course using discussion forums. The OSC also provides access to the course Study Guide and assignments, as well as a selection of electronic journals available on the University of London Online Library.

Objectives and learning outcomes of the course

After studying this course you will be able to:

  • explain the principles of regression analysis
  • outline the assumptions of the classical normal linear regression model, and discuss the significance of these assumptions
  • explain the method of ordinary least squares
  • produce and interpret plots of data
  • use the program Eviews to estimate a regression equation, and interpret the results, for bivariate (two-variable) regression models and multiple regression models
  • test hypotheses concerning model parameters
  • test joint hypotheses concerning more than one variable
  • discuss the consequences of multicollinearity, the methods for identifying multicollinearity, and the techniques for dealing with it
  • explain what is meant by heteroscedasticity, and the consequences for OLS estimators and prediction based on those estimators
  • assess the methods used to identify heteroscedasticity, including data plots and more formal tests, and the various techniques to deal with heteroscedasticity, including model transformations and estimation by weighted least squares
  • explain autocorrelation, and discuss the consequences of autocorrelated disturbances for the properties of OLS estimator and prediction based on those estimators
  • outline and discuss the methods used to identify autocorrelated disturbances, and what can done about it, including estimation by generalised least squares
  • discuss the consequences of disturbance terms not being normally distributed, tests for nonnormal disturbances, and methods to deal with non-normal disturbances, including the use of dummy variables
  • discuss the consequences of specifying equations incorrectly
  • discuss the tests used to identify correct model specification, and statistical criteria for choosing between models
  • use Eviews to conduct tests for heteroscedasticity, correlated disturbances, nonnormal disturbances, functional form, and model selection
  • use Eviews to estimate models in which the disturbance term is assumed to be heteroscedastic or autocorrelated

Scope and syllabus

Course units
Unit 1: Introduction to Econometrics and Regression Analysis
  • 1.1 What is Econometrics?
  • 1.2 How to Use the Course Texts
  • 1.3 Ideas – The Concept of Regression
  • 1.4 Study Guide
  • 1.5 An Example: The Consumption Function
  • 1.6 Summary
  • 1.7 Eviews
  • 1.8 Exercises
  • 1.9 Answers to Exercises
Unit 2: The Classical Linear Regression Model
  • 2.1 Ideas and Issues
  • 2.2 Study Guide
  • 2.3 Example: the Single Index Model (SIM)
  • 2.4 Summary
  • 2.5 Exercises
  • 2.6 Answers to Exercises
Unit 3: Hypothesis Testing
  • 3.1 Ideas and Issues
  • 3.2 Study Guide
  • 3.3 Example: Testing Beta Coefficients
  • 3.4 Summary
  • 3.5 Exercises
  • 3.6 Answers to Exercises
Unit 4: The Multiple Regression Model
  • 4.1 Ideas and Issues
  • 4.2 Study Guide
  • 4.3 Example: a Demand for Money Function
  • 4.4 Summary
  • 4.5 Exercises
  • 4.6 Answers to Exercises
Unit 5: Heteroscedasticity
  • 5.1 Ideas and Issues
  • 5.2 Study Guide
  • 5.3 Example: a Consumption Function
  • 5.4 Summary
  • 5.5 Exercises
  • 5.6 Answers to Exercises
Unit 6: Autocorrelation
  • 6.1 Ideas and Issues
  • 6.2 Study Guide
  • 6.3 Example: The Consumption Function
  • 6.4 Summary
  • 6.5 Exercises
  • 6.6 Answers to Exercises
Unit 7: Nonnormal Disturbances
  • 7.1 Ideas and Issues
  • 7.2 Study Guide
  • 7.3 Examples
  • 7.4 Summary
  • 7.5 Exercises
  • 7.6 Answers to Exercises
  • Appendix 1: Small-Sample Critical Values for the Jarque-Bera Test
  • Appendix 2: Stock Market Indices
Unit 8: Model Selection and Course Summary
  • 8.1 Ideas and Issues
  • 8.2 Study Guide
  • 8.3 Example: the Demand for Money Function
  • 8.4 Summary
  • 8.5 Exercises
  • 8.6 Answers to Exercises
  • 8.7 Course Summary: ‘What You Do and Do Not Know’

Method of assessment

You will complete two assignments, which will be marked by your course tutor. Assignments are each worth 15% of your total mark. You will be expected to submit your first assignment by the Tuesday of Week 5, and the second assignment at the end of the course, on the Tuesday after Week 8. Assignments are submitted and feedback given online. In addition, queries and problems can be answered through the Online Study Centre. You will also sit a three-hour examination on a specified date in October, worth 70% of your total mark. An up-to-date timetable of examinations is published in April of each year.