Description of Individual Course Units
Course Unit CodeCourse Unit TitleType of Course UnitYear of StudySemesterNumber of ECTS Credits
İST407TIME SERIESElective475
Level of Course Unit
First Cycle
Objectives of the Course
The aim of this course provides students to teach basic concepts necessary in time series analysis, as well as to practice the necessary steps for any time series analysis through the package program (stationarity, model identification, parameter estimation, forecasting and so on)
Name of Lecturer(s)
Assoc. Prof. Dr. Sevcan DEMİR ATALAY
Learning Outcomes
1Knowledge of Basic Concepts of Time Series
2Knowledge of Components of Time Series
3Knowledge of Stationary and Nonstationary Stochastic Process Concepts
4To distinguish Stationary and Nonstationary Stochastic Processes
5To comprehend the Properties of Linear Time Series Models
6To comprehend the relation between Autoregressive Process, Moving Average Process and Mixing Process
7Remembering the Stages of Model Building Process
8Interpreting the scatter plot of time series
9Knowledge of Stationarity Analysis
10To make stationary of time series
11Knowledge of the Most Appropriate Model to Data
12Knowledge of Forecasting
13Knowledge of Hypothesis Testing Related with Parameters
14Entering Data to Computer Program
15To be able to apply the steps to make the time series analysis by using packet program
16To interpret the obtained results and knowledge of making inference
Mode of Delivery
Face to Face
Prerequisites and co-requisities
None
Recommended Optional Programme Components
None
Course Contents
Time Series Patterns, Time Series Models, Stationary Stochastic Processes, Nonstationary Stochastic Processes, Linear Time Series Models, Stationarity Analysis: Correlogram Test and Unit Root Test
Weekly Detailed Course Contents
WeekTheoreticalPracticeLaboratory
0Contents, Textbooks. Definition of Time Series, Examples of Time Series in Different Forms, Graphical Approach to Time Series Patterns Presentation of EViews package program
1Time Series Decomposition Method, Data Generation Process, Stochastic Processes: Properties of Stationary Stochastic ProcessEntering a time series to the package program, plotting the series, save file
2Weak, Strong and Strict Stationarity, White Noise, Examples of Non – Stationary Stochastic Process Creation of the data generation process in the package program
3Autoregressive Process: Characteristics of AR(1) Process, Characteristics of AR(2) Process, Characteristics of AR(p) Process, Partial Autocorrelation FunctionDelaying, taking the difference of a time series with the program
4Moving Average Process: Characteristics of MA(1) Process, Characteristics of MA(2) Process, Characteristics of MA(q) ProcessCreation of data generation structures of autoregressive and moving average models
5Autoregressive Moving Average Process: Characteristics of ARMA(1,1) Process, Characteristics of ARMA(p,q) Process, Expression of AR(p), MA(q), ARMA(p,q) Processes Using Lag Operators Creation of data generation structure of autoregressive moving average model
6Homogeneous Non-Stationary Processes: ARIMA (p, d, q), ARIMA Model Building Process: The Box - Jenkins Approach Creation of data generation structure of autoregressive integrated moving average model
7Midterm
8Forecasting: Forecast from AR(1) Model, Forecast from MA(1) Model, Forecast from ARMA(1,1) ModelPrediction of AR, MA, ARMA and ARIMA structures and forecasting
9Mean Stationarity, Variance Stationarity, Difference Stationarity, Trend Stationarity, Integrating Time Series, Covariance and Correlation, Statistical Significance of Correlation Coefficient Related Examples
10Sample Autocorrelation Function: ACF(k), the Statistical Significance of the Sample Autocorrelation Coefficient, Partial Autocorrelation Function: PACF(k), Q - Statistics: Portmanteau TestsRelated Examples
11Correlogram, Correlogram of Time Series Models Plotting the time series Correlograms
12Unit Root Process, Dickey - Fuller (DF) Unit Root Test, Augmented Dickey - Fuller (ADF) Unit Root TestStationarity Analysis of Time Series
13Term ProjectExamples of Time Series Analysis
14Term ProjectTerm Project
15Final Exam
Recommended or Required Reading
Text Book: M. Sevüktekin, M. Nargeleçekenler, “Ekonometrik Zaman Serileri Analizi”, 3. Baskı, Nobel Yayın Dağıtım, ISBN 978-975-591-755-9 References: Yılmaz Akdi, “Zaman Serileri Analizi”, Bıçaklar Kitabevi, ISBN 975-8695-03-7 Course Tools: Eviews Statistical Package Program
Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"
Assessment Methods and Criteria
Term (or Year) Learning ActivitiesQuantityWeight
SUM0
End Of Term (or Year) Learning ActivitiesQuantityWeight
SUM0
SUM0
Language of Instruction
Turkish
Work Placement(s)
None
Workload Calculation
ActivitiesNumberTime (hours)Total Work Load (hours)
Midterm Examination122
Final Examination122
Attending Lectures14228
Practice14228
Individual Study for Mid term Examination13535
Individual Study for Final Examination14545
TOTAL WORKLOAD (hours)140
Contribution of Learning Outcomes to Programme Outcomes
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LO1 4               2      
LO22 3  1      3    32     
LO33 3  3      33   32    2
LO4333  2   3  33   4      
LO5 34  33  3  43   22    2
LO635  434  3  33          
LO7343 433  3  33    25   4
LO8332 2132    3    42     
LO9333 324  3  22   33     
LO10 54 434  4  43   33    4
LO113   434     33   43     
LO124     3     23   4     3
LO13    535        5  3     
LO14    545  3     5  3 4  3
LO153   535  4  44  3 3 2  4
LO16                        
* Contribution Level : 1 Very low 2 Low 3 Medium 4 High 5 Very High
 
Ege University, Bornova - İzmir / TURKEY • Phone: +90 232 311 10 10 • e-mail: intrec@mail.ege.edu.tr