FE5001A - Econometrics (2021/22)
|Module specification||Module approved to run in 2021/22|
|Module status||DELETED (This module is no longer running)|
|Module level||Intermediate (05)|
|Credit rating for module||15|
|School||Guildhall School of Business and Law|
|Total study hours||150|
|Running in 2021/22(Please note that module timeslots are subject to change)||No instances running in the year|
This module focuses on Econometrics, and deals with the theory and application of the Classical Linear Regression Model (CLRM), providing a firm grounding in the theory of Ordinary Least Squares (OLS) and an appreciation of its limitations. It provides a theoretical understanding of the causes, consequences and detection of, and remedies for, the violation of the assumptions of the classical linear regression model. It develops knowledge and skills to use standard statistical/econometric software package (e.g. EViews) and apply techniques to economics, finance and banking problems and models.
The module provides students with the knowledge and skills to design, undertake, and evaluate empirical work within economics, finance and banking.
Students are encouraged to reflect and draw on their diverse socio-cultural
backgrounds and experiences.
Equality is promoted by treating everyone with equal dignity and worth, while also raising aspirations and supporting achievement for people with diverse requirements, entitlements and backgrounds
A range of transferable and subject specific skills are developed, in particular: self- assessment and reflection; peer assessment; written; IT; applied analysis; subject research; problem solving; data and quantitative; analytical and critical thinking.
Prior learning requirements
FE4003 or equivalent
Review of statistics: probability distributions, sampling theory, estimation, confidence intervals, hypothesis testing and applications to economic, finance and banking.
Correlation and regression analysis: applications to economics and finance.
Introduction to Econometrics: economic theory versus empirics.
The Classical Linear Regression Model: assumptions, specification, estimation and hypothesis-testing.
Functional form and non-linearity and dummy variables ALL LO1
Violations of the assumptions of the classical linear regression model: causes, consequences, tests and solutions for multicollinearity, autocorrelation, heteroscedasticity, specification errors.
Use IT to access sources of relevant economic and financial information, and transform into usable information relevant to the analysis of economics, finance and banking.
Development of intermediate knowledge of spreadsheets, using workbooks and solving problems by analysing data. Using and interpreting the output of dedicated econometric software (e.g. EViews), conducting appropriate econometric tests, and writing reports analysing econometric problems. All LO2
Balance of independent study and scheduled teaching activity
Learning consists of ‘formal’ class room learning directed by the teaching team, and reflective independent learning. The formal learning involves lectures, seminars and computer-workshops while the independent learning consists of reading of the course material, working on weekly exercises including computing assignments using software (for example EViews) and coursework that involves undertaking econometric analysis and writing a report.
The module is delivered in a three-hour session each week which comprises a two-hour lecture, and a one-hour seminar or a one-hour computer workshop. In the seminar students present their solution(s) to the problems set and raise questions on the lecture material. In the computer workshop students undertake empirical analysis using IT software. The seminar and the workshop provide opportunities for active and reflective learning, and also formative feedback. A virtual learning environment (WebLearn) supports blended learning by providing module handbook, lecture notes, seminar materials, IT workshop exercises, past test papers with guideline answers, coursework brief with assessment and grading criteria, EViews videos and other learning material.
All activities provide students with knowledge and understanding of econometrics, statistics and financial modelling. The weekly exercises and the coursework give students such diverse skills as working independently, problem solving, writing concisely and clearly, retrieving secondary data from various online sources and describing and exploring them using econometric software and spreadsheets.
Professional and transferable skills are developed in lectures and seminars, and through independent directed learning and assessment. Skills development is enhanced through working cooperatively solving economic problems.
Initiative and independence is developed progressively through the module such that students are required to take greater responsibility of their work.
On successful completion of this module students will be able to:
1. Demonstrate a broad knowledge and a systematic understanding of statistics;
correlation and linear regression analysis; hypothesis testing and application to
economics, finance and banking.
2. Produce evidence, collect, analyse and interpret data and explain regression results; test hypotheses; evaluate regression models and use dedicated statistical and econometric software such as Excel and EViews.
The formative and summative assessments and feedback practices are informed by reflection, consideration of professional practice, and subject-specific knowledge and educational scholarship.
There is a formative peer assessment in week 4 which supports students in developing for summative assessment, in-class test later in the term. It allows students to reflect on their own learning and provide peer feedback.
There are two summative assessments consisting of one In-class test in week 10 assessing learning outcome 1, and an individual coursework (1500 words econometric report) in week 14 assessing learning outcome 2.
Through the summative assessments, students are provided with opportunities to develop an understanding of, and the necessary skills to demonstrate, good academic practice.
The in-class test examines students’ understanding of key principles and concepts developed in the module. The test gives students helpful feedback on their strengths and weaknesses in this technical subject. Before the in-class test, revision activities are carried out to support students’ learning to improve performance.
The coursework is an independent piece of work requiring the application of knowledge gained on the module. It is based on a computing assignment that applies econometric and other quantitative methods to a particular economic/ finance/ banking model using dedicated econometric software. Students write a report and are assessed on their knowledge and skills in designing, executing and evaluating empirical work within a range of economic and financial contexts.
A feed-forward strategy is used to provide early feedback to students to improve their final submission. Use of the feedforward strategy and class discussion of a detailed grading and assessment criteria create an opportunity for dialogue between students and staff and promote shared understanding of the basis on which academic judgements are made.
During seminars, students receive formative feedback on their knowledge and understanding of quantitative techniques and analysis by working though exercises and problems which they prepare before the session. This preparation and feedback provides support for students when they later tackle problems set in summative assessment, in-class test.
Through the summative assessments, students are provided with opportunities to develop an understanding of, and the necessary skills to demonstrate, good academic practice. Written communication, analytical, critical thinking, problem solving, quantitative and interpreting skills are assessed.
All the information about processes of marking and moderating marks, timing of assessments and deadlines for feedback provision are clearly articulated in the module booklet and communicated to students through Weblearn as well.
1. Asteriou, D and Hall S G (2016). Applied econometrics, 3rd ed., Palgrave Macmillan
This is an E-BOOK. Hard copies are available at Aldgate 330.015195 AST
2. Barrow, M. (2013) Statistics for economics, accounting and business studies, 6th
ed., FT Prentice Hall. This is an E-BOOK. Hard copies available at Holloway Rd and
Aldgate 519.502433 BAR
3. Bradley, T. (2007). Essential statistics for economics, business and management, John Wiley & Sons Ltd. Aldgate 519.5 BRA
4. Dougherty, C. (2016). Introduction to econometrics, 5th edition, Oxford.
Aldgate 330.015195 DOU
5. Gujarati, D. N. (2015). Econometrics by example, 2nd ed., Palgrave MacMillan
This is an E-BOOK. Hard copies of earlier editions are available at Aldgate
6. Gujarati, D.N. and Porter D. (2010). Essentials of econometrics, 4th ed., McGraw
Hill. Aldgate 330.015195 GUJ
7. Gujarati, D.N. and Porter D. (2014). Basic econometrics, 6th ed., McGraw Hill
Earlier editions are available at Aldgate 330.015195 GUJ
8. Keller, G. (2012). Managerial statistics, 9th ed., South Western Cengage learning
9. Oakshott, Les (2016). Essential quantitative methods for business, management
and finance, 6th ed. Palgrave Macmillan. This is also available as an E-Book. Hard
copies are available at 658.0015195 OAK Holloway Rd and Aldgate
10. Waters, Donald (2011) Quantitative methods for business, 5th ed., FT Prentice Hall,
This book is available as E-BOOK.
11. Wooldridge, J. M. (2016). Introduction to econometrics, 4th ed., South Western
College Publishing. This is an E-BOOK. Earlier editions are available as hard copies
at Aldgate 330.015195 WOO