COURSE UNIT TITLE

: BUSINESS FORECASTING

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
DBA 6204 BUSINESS FORECASTING ELECTIVE 3 0 0 6

Offered By

Business Administration (English)

Level of Course Unit

Third Cycle Programmes (Doctorate Degree)

Course Coordinator

ASSOCIATE PROFESSOR AYSUN KAPUÇUGIL IKIZ

Offered to

Business Administration (English)

Course Objective

This course provides students the core and advanced techniques for generating and implementing business forecasts. The course is designed to address underlying theory with an overview of actual applications in all fields of management. Students are also having experience in using the techniques on a real forecasting problem.

Learning Outcomes of the Course Unit

1   Describe the main stages and the important issues involved in a forecasting process.
2   Understand the significance of preliminary data analysis and model selection criteria.
3   Analyze real economic, business and financial cross-sectional and time series data by using the appropriate techniques and software with a high level of confidence.
4   Perform a complete business forecast and develop solutions to realistic cases.
5   Have experience writing a report using the language of modern statistics to communicate the forecasting results and explain the managerial implications

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Course Description/Introduction to Forecasting
2 Exploring Data Patterns and an Overview to Forecasting Techniques
3 Forecast Accuracy Measures
4 Forecast Process
5 Moving Averages and Smoothing Methods
6 Time Series and Their Components
7 Article Reviews
8 Simple Regression
9 Multiple Regression Analysis
10 Regression with Time Series Data
11 The Box-Jenkins (ARIMA)-Methodology
12 Article Reviews
13 Judgmental Forecasting and Forecast Adjustments
14 Forecast Project Presentations

Recomended or Required Reading

Text Books:
* Business Forecasting, John E. Hanke and Dean W. Wichern, 9th Edition Pearson Education, 2009.
* Business Forecasting. J. Holton Wilson and Barry Keating, 6th Edition, Irwin/McGrawHill, 2009.
* Forecasting Methods and Applications. Spyros Makridais, Steven C. Wheelwright and Rob. J. Hyndman, 3th Edition or later. John Wiley and Sons Inc.

Software:
* Minitab
* IBM SPSS
* MS Excel

Planned Learning Activities and Teaching Methods

1. Lectures and Class Discussions
2. Computer Applications
3. Homeworks / Case Analysis
4. Article Reviews/Presentations
5. Forecast Project

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 STT TERM WORK (SEMESTER)
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE MTE * 0.20 + STT* 0.40 + FIN* 0.40
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.20 + STT * 0.40 + RST* 0.40


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

1. Lectures will focus on the transfer of basic and advanced forecasting techniques where comprehension is substantially enhanced by additional elaboration and illustration.
2. Exams will measure the ability to identify and apply the appropriate statistic and/or method to forecasting problems in real business environment. Each exam will cover course materials and include problems like those assigned for homework, questions on lecture materials, and additional items covered in class meetings.
3. Homework problems will be assigned frequently. It is imperative that a student works and understands these problems to successfully complete the course. It is strongly recommended the students to work all homework problems as a study tool for the exams. By completing homework assignments, each student will enhance analytical skills, as well as, improve competency utilizing a data analysis add-in tool and/or a statistical package for data entry and analysis. By actively participating in class discussions and in-class assignments, each student will improve communication and analytical skills through learning forecasting concepts and business applications.
4. Case Analysis will offer an excellent opportunity for students to perform statistical analysis and develop solutions to real business forecasting problems.
5. Each student must review and present at least one published article using one of the forecasting techniques covered in the class. Presentation must cover the following main headings:
i. Forecasting Problem,
ii. Collected Data,
iii. Data Analysis and Results (The focus here is on the relevant forecasting method only.)
iv. The limitations of the published research
The presentation should last a maximum of 15 minutes.

6. Students are required to complete a Forecast Project which allows them to apply the forecasting skills they have developed to a topic of personal or professional interest, like analysis of a cross-sectional data or time series from real business environment.
7. Project work can be done individually or in teams of two. Project topics will be determined by the students and are subject to the approval of the instructor.
8. Project reports will enable students improve their competency using the language of statistics to communicate the results. The reports will be evaluated for such factors as apparent understanding of the topic, originality of treatment and discussion, accuracy of results, comprehensiveness of the report s content and depth of the analysis, clarity and mechanics of presentation such as organization, format, punctuation, grammar, and quality of exhibits and charts.
9. Grade for Student Participation will depend on (i) class attendance, (ii) the quality of answers the student provides to questions posed by the instructor during class, and (iii) the general contribution the student makes to the creation of a positive learning environment.
10. A good attendance record may bring the grade up one level, for grades on the boundary between two grade levels.

Language of Instruction

English

Course Policies and Rules

1. It is obligatory to attend at least 70% of the classes.
2. Violations of Plagiarism of any kind will result in disciplinary steps being taken.
3. Absence will not be considered an excuse for submitting homework assignments late.
4. Delayed case and project reports will suffer grade decay equivalent to one letter grade per day late.

Contact Details for the Lecturer(s)

Assoc.Prof. Aysun KAPUÇUGIL IKIZ
aysun.kapucugil@deu.edu.tr

DEU Faculty of Business
Department of Business Administration
Division of Quantitative Methods

Office Hours

To be announced later

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 12 3 36
Student Presentations 2 3 6
Preparations before/after weekly lectures 10 2 20
Preparation for midterm exam 1 15 15
Preparation for final exam 1 15 15
Preparing assignments 8 4 32
Project Preparation 1 25 25
Final 1 3 3
Midterm 1 3 3
TOTAL WORKLOAD (hours) 155

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5
LO.1455
LO.2355
LO.3555
LO.45553
LO.5553