module specification

EC5007 - Empirical Methods in Economics and Finance (2019/20)

Module specification Module approved to run in 2019/20
Module status DELETED (This module is no longer running)
Module title Empirical Methods in Economics and Finance
Module level Intermediate (05)
Credit rating for module 30
School Guildhall School of Business and Law
Total study hours 300
120 hours Scheduled learning & teaching activities
180 hours Guided independent study
Assessment components
Type Weighting Qualifying mark Description
In-Course Test 20%   In Class Test (50 mins)
Coursework 40%   Report ( 2000 words)
Unseen Examination 40%   Unseen Exam (2 hours)
Running in 2019/20 No instances running in the year

Module summary

This module provides an introduction to inferential statistics and regression analysis as applied in Economics and Finance. It aims to provide students with applications of economic theories to real world data allowing them to make judgements about their veracity and adequacy.


Prior learning requirements

EC4007 Quantitative methods for Economics or equivalent.

Module aims

The main aims of the module are to:

  1. Provide a review of statistics and introduce correlation and regression analysis;
  2. Apply statistical techniques, regression and business forecasting analysis to economics and finance;
  3. Examine the relationship between real data and economic theories;
  4. Introduce dedicated econometric software such as Eviews and interpret the output;
  5. Develop analytical, problem solving and report writing skills.

The module also aims to develop students' skills, in particular: literacy; communication, including oral presentation; academic study skills; problem solving; subject research; IT; applied analysis; quantitative analysis; and data analysis.



Introduction to empirical methods and econometrics: economic theory versus empirics.
Review of  statistics:  probability distributions, Sampling theory, estimation and confidence intervals and applications to economics and finance.
Development of IT quantitative software including spreadsheets. Using workbooks. Organising and managing data including sorting and filters. Solving problems by analysing data. Solving what-if problems. Develop digital literacy including downloading, reformatting, and refining data, analysing data, presenting data, and intermediate spreadsheet operation.
Hypothesis testing and applications to economic and finance.
Correlation and regression analysis and applications to economics and finance.
The classical linear regression model: specification, estimation, hypothesis-testing and forecasting.
Use of dedicated econometric software such as EViews and interpretation of the output
Multiple regression analysis, estimation, hypothesis testing and forecasting.
Functional form and non-linearity: dummy variables and transformation of variables.
Violations of the assumptions of the classical model: autocorrelation, heteroscedasticity, measurement error, multicollinearity and specification errors.
Dynamic models: distributed lag models.
Simultaneous-equations models: basic issues, identification and estimation methods.
Applied business forecasting and applications to economics and finance
The preparation, development and presentation of professional business reports.

Learning and teaching

The module is delivered in a four-hour session each week for a thirty-week period. The four-hour weekly session comprises a two-hour lecture/whole group session, a one-hour seminar and a one-hour computer workshop. In the seminar students present solutions to problem set and raise questions on the lecture material. In the computer workshop students undertake empirical analysis using EViews. A virtual learning environment (Weblearn) will support learning and teaching activities by providing lecture notes, seminar material, data sets, past exam papers and other learning materials.


Learning outcomes

On successful completion of the module, students will be able to:

  1. demonstrate an understanding of fundamental statistical concepts including probability and probability distributions, random variables, and distinction between a population and a sample;
  2. comprehend the nature of statistical estimation, hypothesis testing and business forecasting and their applications to economics and finance;
  3. explain clearly the relationship between economic theory and empirics;
  4. identify and explain some of the limitations of econometrics as a method of relating economic theory to observation;
  5. design, undertake, and evaluate empirical work in economics/business/finance and carry out independent and scholarly research, including to investigate and use knowledge to provide analysis and evaluation of specific issues and problems related to the analysis of economic, business and financial problems;
  6. use a dedicated econometrics software and spreadsheets and interpret the output competently.      


Assessment strategy

The assessment strategy is developed with the aim of testing the module's learning outcomes. Students will be assessed by both formative and summative assessment through in class test, coursework and unseen examination.

The coursework is an empirical economics and finance report that applies econometric methods to a particular economic model or business issue using dedicated statistical/econometric software. Students will use real data, apply an economic model using regression analysis and present findings in a report. The coursework will require students to: show in-depth subject knowledge and understanding; show competence in using a dedicated econometric software and interpreting the output, use cognitive skills of analysis, evaluation, interpretation and application; employ a range of sources; be focused and accurate; be critically aware; present findings clearly and convincingly; demonstrate a high degree of originality, creativity and independence of learning.

The in-class test and unseen written examination will primarily provide a thorough assessment of  knowledge of theory and the application, interpretation and method of econometrics; ability to solve problems in economics and finance, evaluate, interpret and explain statistical and econometric results.


Asteriou, D. and Hall S. G. (2011) Applied Econometrics, 2nd ed., Palgrave Macmillan
Barrow, M. (2009) Statistics for Economics, Accounting and Business Studies, 5th ed., FT Prentice Hall
Bradley, T. (2007) Essential Statistics for Economics, Business and Management, John Wiley & Sons Ltd.
Brooks, C. (2008) Introductory Econometrics for Finance, 2nd ed., CUP
Gujarati, D. N. (2011) Econometrics by Example, Palgrave MacMillan
Gujarati, D.N. and Porter D. (2010) Essentials of Econometrics, 4th ed., McGraw Hill
Gujarati, D.N. and Porter D. (2009) Basic Econometrics; 5th ed., McGraw Hill
Keller, G. (2009) Managerial Statistics, 8th ed., South Western Cengage learning