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SSST Subjects

EC400 Business Forecasting

 
 

Programme(s) where module is offered

  • BSc in Economics with International Business
  • BSc in Economics with Finance
  • BSc in Business and Management with Finance
  • BSc in Business and Management with Economics
 

Status (core, option, free choice)

Core

 

FHEQ Level

6

 

Unit Value

8 ECTS

 

Semester taught

Autumn

 

Pre-Requisite Modules or Qualifications

EC281 Introduction to Econometrics

 

Module Code

EC400

 

Module coordinator

TBD

 

Applicable From

2017

 

Educational Aims of the Module

  • The module covers methods of forecasting business and economics variables such as revenues, cash flow, interest rates etc. After reviewing statistical concepts underlying all forecasting methods and models, the module introduces students to Auto Regressive Integrated Moving Average (ARIMA) models and their extensions required to take into account trends, seasonality and business cycles.
  • Since every forecast is associated with uncertainty, the module covers methods (ARCH/GARCH) for forecasting uncertainty.
  • After covering singe variable forecasting methods, the module considers analysis and forecasting in the multi-variable setting (Vector Auto Regressive and Vector Error Correction models), which allows to capture interaction among different variables such as prices of completing firms.
  • In the final part, the module covers models for forecasting probabilities (e.g. the probability to switch to a competing products) and forecasting market shares) and simulations
 

Module Outline/Syllabus

  • Introduction to forecasting models
  • Time series and stationarity
  • Time series forecasting: Auto Regressive Integrated Moving Average (ARMA) models
  • Uncertainty forecasting: Auto Regressive Conditional heteroscedasticity (ARCH) models
  • Multiple variable forecasting: Vector Auto Regressive Models (VAR)
  • Multiple variable forecasting: Cointegration and Vector Error Correction models (VEC)
  • Combining cross-section and time dimensions: Panel data models
  • Correlation of errors in panel data models
  • Forecasting probabilities: Binary/ordinal variables as dependent variables (Probit/Logit models)
  • Separating effects of interest: Instrumental variables
  • Simulations
 

Student Engagement Hours

Type Number per Term Duration Total Time

Lectures

15 1.5 22.5

Workshops

10 1.5 15.0

Seminars

5 1.5 7.5
Tutorials 5 1.5 7.5
Total Guided/Independent Learning Hours

155.0

Total Contact Hours

45.0

Total Engagement Hours 200.0
 

Assessment Method Summary

Type Number Required Duration / Length Weighting Timing / Submission Deadline

Final exam

1

3 hours

50%

Week 17

Mid-term exam

1

2 hours

20%

Week 8

Practical (Computer Lab) open-book subject test

1

1 hour

15%

Weeks 4 and 12

 

Module Outcomes

Intended Learning Outcomes:

  • Ability to critically analyse functional relationships between variables and forecast future values of business variables

  • Ability to critically evaluate models’ assumptions and consequences of violation of these assumptions

  • Understanding limits of forecasting

  • Ability to critically analyse relationships between variables outside the context studied in the class

  • Conceptual understanding of the theory underlying advanced forecasting models

  • Ability to use already defined methods to conduct a research with the aim of collecting new information and to critically analyse the results.

Teaching and Learning Strategy:

  • Lectures will be based on the primary book, combined with additional resources. (ILO: 1-5)

  • In-class case studies will enable students to relate theory learned in the class to practical examples and application. (ILO: 1-5)

  • Computer lab exercises will be focused on practical application of gained knowledge. (ILO: 1-3)

  • Group project will encourage research activities related to the Business Forecasting as well as the team work. (ILO: 5)

Assessment Strategy:

  • Midterm exam (ILO: 1-3)

  • Final exam (ILO: 4-5)

  • Computer based test (ILO: 1-4)

Practical Skills:

  • Ability to interpret results of empirical studies based on advanced regression models

  • Ability to quantify how a change in one variable impacts upon another variable

  • Ability to design and complete a research project

Teaching and Learning Strategy:

  • Case studies in lectures and tutorials (PS:1-2)

  • Practical computer lab sessions (PS: 3)

Assessment Strategy:

  • Midterm exam and practical (computer lab) subject test (PS:1-2)

  • Computer based tests (PS: 3)

Transferable Skills:

  • A problem-centered and problem-solving approach
  • Ability to effectively present own and others’ point of view
  • Numeracy Skills
  • Communication Skills: Written and Oral
  • Research Skills
  • Data analyses skills
  • Ability to work independently
  • Ability to work in the team
  • Classify data
  • Compare, inspect or record facts
  • Meet deadlines
  • Good time management
  • Organize/manage projects
  • IT Skills

Teaching and Learning Strategy:

  • Tutorials will provide a platform for in depth analysis and topical discussion (TS: 1-4, 6-10)

  • Computer lab sessions will enable students to learn how to use an econometrics software (Eviews) to practically estimate models and interpret the results on their own.(TS: 1,3,6,7,9,10)

  • Group project (TS:1-6, 8-14)

Assessment Strategy:

  • Computer based test (TS:1-6, 8-14)

  • Written exams (TS: 1-4, 7, 6-10)

 

Key Texts and/or other learning materials

Set text

  • Gujarati, D. (2014), Econometrics by Example, 2 Edition, Palgrave Macmillan

Supplementary Materials

Please note

This specification provides a concise summary of the main features of the module and the learning outcomes that a typical student might reasonably be expected to achieve and demonstrate if he/she takes full advantage of the learning opportunities that are provided.

More detailed information on the learning outcomes, content and teaching, learning and assessment methods of each module and programme can be found in the departmental or programme handbook.

The accuracy of the information contained in this document is reviewed annually by the University of Buckingham and may be checked by the Quality Assurance Agency.

Date of Production : May 2017

Date approved by School Learning and Teaching Committee

Date approved by School Board of Study

Date approved by University Learning and Teaching Committee

Date of Annual Review

       
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