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Centre for Financial and Management Studies (CeFiMS)

Econometric Analysis & Applications

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'Econometric Analysis & Applications' is the second, more advanced, econometrics course offered to those wanting to broaden their understanding of the application of quantitative methods to economic inquiry. We recommend that you study the 'Econometric Principles & Data Analysis' course prior to this.

The course assumes that you have studied the classical linear regression model at an introductory level and that you are familiar with the assumptions that underlie that model. You will be aware that there are many cases in which these assumptions are not satisfied, and know how such problems as heteroscedastic disturbances and autocorrelated errors can be detected, and what can be done about them.

It is assumed, too, that you have a basic working knowledge of the econometric software, Eviews, introduced previously in 'Econometric Principles and Data Analysis', although basic instructions for using the program are provided here as well.


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 (2009) Basic Econometrics, Fifth Edition, New York: McGraw-Hill

Econometric software

You should have already received a copy of EViews 6, the software package for university econometrics courses designed for Windows users, with the course materials for the course Econometric Principles & Data Analysis. If you have not yet taken this course prior to studying Econometric Analysis & Applications, you will be sent the application and instructions on how to use it along with the rest of the course materials.

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:

  • specify dummy variables to measure qualitative influences in regression analysis
  • explain the use of intercept and slope dummy variables
  • use and interpret the Chow test of parameter stability
  • explain the nature of the ‘dummy variable trap’ and how to avoid it
  • explain finite distributed lag models, including immediate impact, longrun reactions and mean lag
  • implement the Koyck transformation
  • explain and discuss the adaptive expectations hypothesis and its limitations
  • discuss the properties of estimators of distributed lag and autoregressive models
  • implement both Durbin's h test and the LM test of autocorrelation and interpret the results
  • explain and implement the Granger test of causality
  • explain 'simultaneous equation bias'
  • interpret in a model the behavioural equations, definitions or identities, and equilibrium conditions
  • identify conditions for stability in dynamic simultaneous equation systems
  • explain the identification problem
  • discuss the implications of equations which are exactly identified, overidentified, and not identified
  • explain and apply indirect least squares
  • explain the properties of the OLS estimator of the slope coefficients of a structural equation from a simultaneous system
  • explain the method of ILS, implement it in appropriate situations, and discuss the properties of ILS estimators
  • explain and discuss the method of two-stage least squares (TSLS or 2SLS), implement it for an identified equation with Eviews, and outline the properties of 2SLS estimators.
  • explain and discuss trended series, and stationary and nonstationary series and to understand and
  • use graphical techniques and more formal tests for stationarity
  • explain the nature of cointegration and the relationship between spurious regression and cointegration and to discuss and implement tests of cointegration
  • understand and discuss forecasting with econometric models, including static and dynamic single equation models and simultaneous equation models.
  • estimate time series models and use them for forecasting.

Scope and syllabus

Course Units
Unit 1: Dummy Variables
  • 1.1 Introduction
  • 1.2 The Use of Dummy Variables
  • 1.3 The Chow Test for Parameter Stability
  • 1.4 Study Guide
  • 1.5 Example: Long-Term Trends in Terms of Trade
  • 1.6 Summary
  • 1.7 Exercises
  • 1.8 Answers to Exercises
Unit 2: Dynamic Models: Lags and Expectations
  • 2.1 Ideas and Issues
  • 2.2 Lags
  • 2.3 Expectations
  • 2.4 Properties of OLS Estimators
  • 2.5 Causality: The Granger Test
  • 2.6 Study Guide
  • 2.7 Example: Money Demand
  • 2.8 Summary
  • 2.9 Exercises
  • 2.10 Answers to Exercises
Unit 3: Simultaneous Equation Models
  • 3.1 Ideas and Issues
  • 3.2 Study Guide
  • 3.3 Example: The Polak Model
  • 3.4 Summary
  • 3.5 Exercises
  • 3.6 Answers to Exercises
Unit 4: The Identification Problem
  • 4.1 Ideas and Issues
  • 4.2 Study Guide
  • 4.3 Example: a Model of the Effects of Taxation on Rice Exports
  • 4.4 Summary
  • 4.5 Exercises
  • 4.6 Answers to Exercises
  • Appendix A: Estimates of Moore’s 1914 Model
Unit 5 Simultaneous Equation Models: Estimation
  • 5.1 Ideas and Issues
  • 5.2 Study Guide
  • 5.3 Examples
  • 5.4 Summary
  • 5.5 Exercises
  • 5.6 Answers to Exercises
  • Appendix A: Obtaining 2SLS Estimates with EViews 6
Unit 6: Univariate Time Series: Stationarity and Nonstationarity
  • 6.1 Ideas
  • 6.2 Stationary and Nonstationary Time Series
  • 6.3 Integrated and Trend-Stationary Series
  • 6.4 The Nature of Financial Data
  • 6.5 Correlograms
  • 6.6 Unit Root Tests
  • 6.7 Examples
  • 6.8 A Procedure for Unit Root Tests
  • 6.9 Summary
  • 6.10 Exercises
  • 6.11 Answers to Exercises
Unit 7: Multivariate Time Series Analysis
  • 7.1 Ideas and Issues
  • 7.2 The Engle-Granger Approach
  • 7.3 Error Correction Models
  • 7.4 The Johansen Approach
  • 7.5 Example: The Single Index Model
  • 7.6 Example: UK Financial Markets
  • 7.7 Summary
  • 7.8 Exercises
  • 7.9 Answers to Exercises
Unit 8: Forecasting
  • 8.1 Ideas and Issues
  • 8.2 Example: Forecasting Earnings and Dividends
  • 8.3 Summary
  • 8.4 Exercises
  • 8.5 Answers to Exercises

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.