 ### Introduction

This module provides an introduction to econometric methods. In brief, the module examines how we can start from relationships suggested by financial and 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 module 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 the estimated models. In addition, the module explores what happens when these assumptions are not satisfied, and what we can do in these circumstances. The module concludes with an examination of model selection.

We recommend that you take this module before progressing onto the more advanced econometrics module Econometric Analysis and Applications.

### Learning outcomes

After studying this module you will be able to:

• explain the principles of regression analysis
• discuss the assumptions of the classical normal linear regression model
• explain the method of ordinary least squares
• produce and interpret plots of data
• estimate a regression equation, and interpret the results, for bivariate (two-variable) regression models and multiple regression models
• test hypotheses concerning model parameters
• assess the consequences of multicollinearity
• discuss the consequences of heteroscedasticity for the properties of OLS estimators
• assess the methods used to identify heteroscedasticity, and the various techniques to deal with heteroscedasticity
• discuss the consequences of autocorrelated disturbances for the properties of OLS estimators
• outline and discuss the methods used to identify autocorrelated disturbances, and what can be done about it
• assess the consequences of disturbance terms not being normally distributed, tests for nonnormal disturbances, and methods to deal with non-normal disturbances
• examine the consequences of specifying equations incorrectly
• discuss the tests used to identify correct model specification, and statistical criteria for choosing between models.

### Study resources

###### Study guide

The module study guide is 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 assigned readings.

###### Key texts

Wooldridge JM (2020) Introductory Econometrics. 7th Edition. Boston MA: Cengage.

###### Econometric software

This module will use R. This is a widely used programming environment for data analysis and graphics. You will use this software to do the exercises in the units, and also the data analysis part of your assignments. The results presented in the units are also from R.

###### Virtual learning environment

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

##### Unit 1 An Introduction to Econometrics and Regression Analysis
• 1.1 What is Econometrics?
• 1.2 How to Use the Module Units
• 1.3 Ideas – The Concept of Regression
• 1.4 Study Guide
• 1.5 An Example – Efficiency in the Foreign Exchange Market
• 1.6 Conclusion
• 1.7 Working with R
• 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 Conclusion
• 2.5 Exercises
• 2.6 Answers to Exercises
• Appendix 2.1: Derivation of OLS estimators
##### Unit 3 Hypothesis Testing
• 3.1 Ideas and Issues
• 3.2 Study Guide
• 3.3 Example – The Capital Asset Pricing Model
• 3.4 Conclusion
• 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 Multi-index Model
• 4.4 Conclusion
• 4.5 Exercises
• 4.6 Answers to Exercises
##### Unit 5 Heteroscedasticity
• 5.1 Ideas and Issues
• 5.2 Study Guide
• 5.3 Example – Price-Earnings Ratio
• 5.4 Conclusion
• 5.5 Exercises
• 5.6 Answers to Exercises
##### Unit 6 Autocorrelation
• 6.1 Ideas and Issues
• 6.2 Study Guide
• 6.3 Example – The Single-Index Model
• 6.4 Conclusion
• 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 Conclusion
• 7.5 Exercises
• 7.6 Answers to Exercises
• Appendix 7.1: Small-Sample Critical Values for the Jarque-Bera Test
• Appendix 7.2: Stock Market Indices
##### Unit 8 Model Selection and Module Summary
• 8.1 Ideas and Issues
• 8.2 Study Guide
• 8.3 Example: Stock Returns
• 8.4 Conclusion
• 8.5 Exercises
• 8.6 Answers to Exercises
• 8.7 Module Summary: ‘What you do and do not know’

### Tuition and assessment

Students are individually assigned an academic tutor for the duration of the module, with whom you can discuss academic queries at regular intervals during the study session.

You are required to complete two Assignments for this module, which will be marked by your tutor. Assignments are each worth 15% of your total mark. You will be expected to submit your first assignment by the Tuesday of Week 6, and the second assignment at the end of the module, on the Tuesday after Week 10. Assignments are submitted and feedback given online. In addition, queries and problems can be answered through the Virtual Learning Environment.

You will also sit a three-hour examination on a specified date in September/October, worth 70% of your total mark. An up-to-date timetable of examinations is published on the website in July each year.

### Module samples

Click on the links below to download the module sample documents in PDF.