
Description of Individual Course UnitsCourse Unit Code  Course Unit Title  Type of Course Unit  Year of Study  Semester  Number of ECTS Credits  İST407  TIME SERIES  Elective  4  7  5 
 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  1  Knowledge of Basic Concepts of Time Series  2  Knowledge of Components of Time Series  3  Knowledge of Stationary and Nonstationary Stochastic Process Concepts  4  To distinguish Stationary and Nonstationary Stochastic Processes  5  To comprehend the Properties of Linear Time Series Models  6  To comprehend the relation between Autoregressive Process, Moving Average Process and Mixing Process  7  Remembering the Stages of Model Building Process  8  Interpreting the scatter plot of time series  9  Knowledge of Stationarity Analysis  10  To make stationary of time series  11  Knowledge of the Most Appropriate Model to Data  12  Knowledge of Forecasting  13  Knowledge of Hypothesis Testing Related with Parameters  14  Entering Data to Computer Program  15  To be able to apply the steps to make the time series analysis by using packet program  16  To interpret the obtained results and knowledge of making inference 
 Mode of Delivery  Face to Face  Prerequisites and corequisities  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  
0  Contents, Textbooks. Definition of Time Series, Examples of Time Series in Different Forms, Graphical Approach to Time Series Patterns
 Presentation of EViews package program   1  Time Series Decomposition Method, Data Generation Process, Stochastic Processes: Properties of Stationary Stochastic Process  Entering a time series to the package program, plotting the series, save file   2  Weak, Strong and Strict Stationarity, White Noise, Examples of Non – Stationary Stochastic Process
 Creation of the data generation process in the package program   3  Autoregressive Process: Characteristics of AR(1) Process, Characteristics of AR(2) Process, Characteristics of AR(p) Process, Partial Autocorrelation Function  Delaying, taking the difference of a time series with the program   4  Moving Average Process: Characteristics of MA(1) Process, Characteristics of MA(2) Process, Characteristics of MA(q) Process  Creation of data generation structures of autoregressive and moving average models   5  Autoregressive 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   6  Homogeneous NonStationary Processes: ARIMA (p, d, q), ARIMA Model Building Process: The Box  Jenkins Approach  Creation of data generation structure of autoregressive integrated moving average model   7  Midterm    8  Forecasting: Forecast from AR(1) Model, Forecast from MA(1) Model, Forecast from ARMA(1,1) Model  Prediction of AR, MA, ARMA and ARIMA structures and forecasting   9  Mean Stationarity, Variance Stationarity, Difference Stationarity, Trend Stationarity, Integrating Time Series, Covariance and Correlation, Statistical Significance of Correlation Coefficient  Related Examples   10  Sample Autocorrelation Function: ACF(k), the Statistical Significance of the Sample Autocorrelation Coefficient, Partial Autocorrelation Function: PACF(k), Q  Statistics: Portmanteau Tests  Related Examples   11  Correlogram, Correlogram of Time Series Models  Plotting the time series Correlograms   12  Unit Root Process, Dickey  Fuller (DF) Unit Root Test, Augmented Dickey  Fuller (ADF) Unit Root Test  Stationarity Analysis of Time Series   13  Term Project  Examples of Time Series Analysis   14  Term Project  Term Project   15  Final Exam   
 Recommended or Required Reading  Text Book:
M. Sevüktekin, M. Nargeleçekenler, “Ekonometrik Zaman Serileri Analizi”, 3. Baskı, Nobel Yayın Dağıtım, ISBN 9789755917559
References:
Yılmaz Akdi, “Zaman Serileri Analizi”, Bıçaklar Kitabevi, ISBN 9758695037
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   Language of Instruction  Turkish  Work Placement(s)  None 
 Workload Calculation 

Midterm Examination  1  2  2  Final Examination  1  2  2  Attending Lectures  14  2  28  Practice  14  2  28  Individual Study for Mid term Examination  1  35  35  Individual Study for Final Examination  1  45  45  
Contribution of Learning Outcomes to Programme Outcomes  LO1   4                 2        LO2  2   3    1        3      3  2       LO3  3   3    3        3  3     3  2      2  LO4  3  3  3    2     3    3  3     4        LO5   3  4    3  3    3    4  3     2  2      2  LO6  3  5    4  3  4    3    3  3            LO7  3  4  3   4  3  3    3    3  3      2  5     4  LO8  3  3  2   2  1  3  2      3      4  2       LO9  3  3  3   3  2  4    3    2  2     3  3       LO10   5  4   4  3  4    4    4  3     3  3      4  LO11  3     4  3  4       3  3     4  3       LO12  4       3       2  3     4       3  LO13      5  3  5          5    3       LO14      5  4  5    3       5    3   4    3  LO15  3     5  3  5    4    4  4    3   3   2    4  LO16                         
 * Contribution Level : 1 Very low 2 Low 3 Medium 4 High 5 Very High 



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