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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS EMT 4012 ECONOMETRICS SEMINAR ELECTIVE 3 0 0 5

Offered By

Econometrics

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSOCIATE PROFESSOR HAMDI EMEÇ

Offered to

Econometrics Econometrics (Evening)

Course Objective

To gain knowledge of econometric on the top level about the theory of time series analysis and tecniques and to provide acquisition of practical experience on the provision of economic time series.

Learning Outcomes of the Course Unit

1   To be able to define time series analyis 2   To be able to apply appropriate methods for time series 3   To be able to estimate future and interpret the results by using economic time series data. 4   To be able to organize data in research 5   To be able to set up model by using appropriate software 6   To be able to modelling time series encountering in public and private sector and to do statistical implications of these models.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 The Simple Approach to Exponential Smoothing 2 The Holt-Winters Approach to Exponential Smoothing 3 Stationary and Nonstationary Time Series, autocorrelation and partial autocorrelation functions 4 Stationary ARIMA Models: Autoregressive models 5 Stationary ARIMA Models: Moving Average models 6 Stationary ARIMA Models: Autoregressive Integrated Moving Average models 7 Stationary ARIMA Models: The Application 8 Mid-term 9 Mid-term 10 Non-Stationary ARIMA Models: ARIMA models 11 Non-Stationary ARIMA Models: Box-Jenkins Models 12 Non-Stationary ARIMA Models: The Application 13 Seasonal ARIMA models 14 Application of Seasonal ARIMA models

Recomended or Required Reading

1- SPSS Uygulamalı Zaman Serileri Analizine Giriş, Dr. Cem KADILAR 2- Zaman Serilerinin Analizi ve ARIMA Modelleri, Prof. Dr. Işıl AKGÜL 3- Ekonometrik Zaman Serileri Analizi, Prof.Dr. Mustafa SEVÜKTEKIN, Dr. Mehmet NARGELEÇEKENLER

Planned Learning Activities and Teaching Methods

This course will be presented using softwares to set up econometrics models

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 *** 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)

Asst. Prof. Hamdi EMEÇ E-mail: hamdi.emec@deu.edu.tr Tel: 0 (232) 301 03 58 Int: 10358

Office Hours

Monday: 16:00 - 18:00

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 12 2 24 Case study 12 1 12 Preparations before/after weekly lectures 12 2 24 Preparation for midterm exam 1 25 25 Preparation for final exam 1 28 28 Preparation for quiz etc. 2 3 6 Midterm 1 1 1 Final 1 1 1 Quiz etc. 2 1 2 TOTAL WORKLOAD (hours) 123

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 1 1 LO.3 1 1 1 1 1 1 LO.4 1 1 LO.5 1 1 LO.6 1 1 1 1 1