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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS EMT 2022 TIME SERIES ANALYSIS COMPULSORY 3 0 0 3

Offered By

Econometrics

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSOCIATE PROFESSOR HAMDI EMEÇ

Offered to

Econometrics (Evening) Econometrics

Course Objective

This course aims to equip students with a comprehensive understanding and applied skills in time series analysis, enabling them to analyze historical data trends and forecast future patterns effectively.

Learning Outcomes of the Course Unit

1   Explain the basic concepts in time series analysis 2   Analyze time series data 3   Establish and forecast time series models 4   Apply data smoothing and transformation techniques 5   Solve problems encountered in time series analysis 6   Conduct practical applications on real-world data

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 A general overview of time series and the examination of time series data 2 Examination of time series components and time series graphs, lag number, autocorrelation, and partial autocorrelation functions. 3 Investigation of differencing operations, stationarity, white noise series, and error term concepts. 4 Smoothing of time series: Examination of simple and centered moving averages. 5 Examination of index numbers. 6 The concept of real terms, per capita concept, and the examination of missing data. 7 Midterm Exam. 8 Midterm Exam. 9 Decomposition Methods: Additive decomposition method, the validity of the method, and confidence intervals. 10 Decomposition Methods: Multiplicative decomposition method, the validity of the method, and confidence intervals. Seasonality Test. 11 Exponential Smoothing Methods: Simple exponential smoothing method. 12 Exponential Smoothing Methods: Holt's exponential smoothing method. 13 Exponential Smoothing Methods: Winter's exponential smoothing method. 14 Applications.

Recomended or Required Reading

SPSS ve R Uygulamalı Zaman Serileri Analizine Giriş Brooks, Chris, Econometrics for Finance, Cambridge, 2008. Verbeek, Marno, Modern Econometrics, wiley, 2008. Hill, R.Carter, William E.Griffiths, Guay .C.Lim, Principles of Econometrics, Wiley, 2008

Planned Learning Activities and Teaching Methods

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 MTE MIDTERM EXAM 2 MTEG MIDTERM GRADE MTEG * 1 3 FIN FINAL EXAM 4 FCGR FINAL COURSE GRADE MTEG * 0.40 + FIN * 0.60 5 RST RESIT 6 FCGR FINAL COURSE GRADE (RESIT) MTEG * 0.40 + RST * 0.60

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)

Associate Professor Hamdi Emeç: hamdi.emec@deu.edu.tr

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 12 3 Preparations before/after weekly lectures 12 1 Preparation for midterm exam 1 10 Preparation for final exam 1 15 Independant Study 1 10 Midterm 1 1 Final 1 1 TOTAL WORKLOAD (hours) 0

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 PO.9 PO.10 LO.1 1 LO.2 1 LO.3 1 LO.4 1 LO.5 1 LO.6 1