• HOME
• ABOUT US
  • Name Address
  • General Description
  • Governance
  • Academic Calendar
  • Degree Programmes
  • List of Programmes in English
  • General Admission and Registration Procedures
  • Credit Mobility and Recognition of Prior Learning
  • ECTS Credit Allocation
  • Academic Guidance
  • Contacts
• Degree Programmes
  • Third Cycle Programmes (Doctorate Degree)
  • Second Cycle Programmes (Master's Degree)
  • First Cycle Programmes (Bachelor's Degree)
  • Short Cycle Programmes (Associate's Degree)
• GENERAL INFORMATION FOR STUDENTS
  • Cost of Living
  • Accommodation
  • Meals
  • Medical Facilities
  • Facilities for Disabled Students
  • Insurance
  • Financial Supports
  • Student Affairs Office
  • Practical Information for International Students
  • Language Courses
  • Work Placement Possibilities
  • Sports, Social and Cultural Facilities
  • Student Associations and Clubs
  • About İzmir - Turkey
• INTERNATIONAL RELATIONS
  • International Relations Office
  • Registration Procedures
  • Agreements
  • Coordinators
  • Documents
  • List of Programmes in English
  • ECTS / DS Labels

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS EKO 5012 FORECASTING METHODS ELECTIVE 3 0 0 4

Offered By

Econometrics

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

Offered to

Econometrics

Course Objective

Classification of forecasting methods used in business and economics sciences, features of forecasting methods, forecasting method selection, model selection, forecasting model, testing the validity of the model and checking model

Learning Outcomes of the Course Unit

1   To identify the appropriate forecasting method. 2   To determine properties of the resulting predictions 3   Obtaining predictions from sampling method 4   Be able to test the validity of the obtained predictions 5   Be comparable in prediction methods. 6   To classify forecasting methods

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Forecasting methods, classification and the appropriate method selection 2 Exponential smoothing method in non-seasonal data: fixed averaged model, single-stage exponential smooth 3 The two-stage and three-stage exponential smoothing method 4 Exponential smoothing method in seasonal data 5 Winter's method and seasonal adjustment 6 Stochastic time series models: Autoregressive model 7 Moving Average Model 8 Mid-term 9 Autoregressive moving average model 10 Stationarity, transformations and seasonal autoregressive moving average model 11 Least squares estimation, maximum likelihood estimation, nonlinear estimation 12 Diagnosis and control of the model, multiplicative seasonal models 13 The relationships between the general exponential smoothing and ARIMA time series models 14 Applications

Recomended or Required Reading

Statistical Methods for Forecasting, B. Abraham ve J. Ledorter

Planned Learning Activities and Teaching Methods

Lecture Method, Question-Answer Method, Method Discussion and Problem Solving Method.

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 * 0.30 + STT * 0.30 + FIN* 0.40 5 RST RESIT 6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.30 + STT * 0.30 + RST* 0.40 *** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

To be announced.

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 13 3 39 Preparations before/after weekly lectures 13 2 26 Preparation for midterm exam 1 20 20 Preparation for final exam 1 20 20 Midterm 1 3 3 Final 1 3 3 TOTAL WORKLOAD (hours) 111

Contribution of Learning Outcomes to Programme Outcomes

PO/LO PO.1 PO.2 PO.3 PO.4 PO.5 PO.6 PO.7 PO.8 LO.1 1 LO.2 1 LO.3 1 LO.4 1 LO.5 1 1 1 LO.6 1 1