Course Details

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Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
1DSBE513TIME SERIES ANALYSIS3+0+037,513.05.2025

 
Course Details
Language of Instruction English
Level of Course Unit Master's Degree
Department / Program DATA SCIENCE
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course Introducing time series data, its characteristics and related exploratory data
analysis techniques.
Introducing types of questions that are analyzed with time series data in various
fields of economics, business and social sciences.
Developing an understanding of how time series data analysis differs from other
data analysis techniques and why it is necessary in specific cases.
Course Content Main aim of the course is to teach time series analysis techniques and their
applications to analysis and estimation of time series data at graduate level.
Autocovariance, autocorrelation, stationary and non-stationary time series, unit root
tests, cointegration tests, causality tests and related topics are examined throughout
the course. By the end of the course students are expected to have knowledge for
identifying the necessity of using time series data in various disciplines. In addition,
students are expected to be able to test and assess validities of various models used
in analyses with time series data.
Course Methods and Techniques -
Prerequisites and co-requisities None
Course Coordinator Associate Prof.Dr. Umut Türk umut.turk@agu.edu.tr
Name of Lecturers None
Assistants Research Assist. Semih Peker semih.peker@agu.edu.tr
Work Placement(s) No

Recommended or Required Reading
Resources -
Course Notes -


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
In-Term Studies Quantity Percentage
Yarıyıl İçi Çalışmalarının Başarı Notunun Katkısı 1 % 40
Final examination 1 % 60
Total
2
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Belirsiz 6 30 180
Araştırma Ödevi 1 15 15
Yazılı Sınav 1 30 30
Total Work Load   Number of ECTS Credits 7,5 225

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Identify questions necessitating time series analysis.
2 Identify main issues present with time series data: stationarity, autocorrelation, structural breaks, length, seasonality.
3 Conduct exploratory data analysis to characterize time series data sets. Differentiate between AR, MA, ARMA, ARIMA, VAR, ARCH, GARCH, GMM models in time series setting.
4 Apply time series estimation techniques in order to analyze specific research questions.
5 Interpret time series estimation results statistically and in relation to discipline-specific significance.

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Week 1: Introduction to Time Series Data and Its Importance • What is a time series? Examples across disciplines • Types of time series: univariate, multivariate, panel vs. pure time series • Temporal dependence vs. cross-sectional independence • Software introduction (e.g., R or Stata) • Assignment: Exploratory analysis of a real-world time series dataset - -
2 Week 2: Graphical and Numerical Description of Time Series • Trend, seasonality, cycles, irregular components • Decomposition methods • Autocorrelation (ACF) and Partial Autocorrelation (PACF) • Lab: Plotting and interpreting ACF/PACF in software • Reading: Chapter from Cryer & Chan or Hamilton - -
3 Week 3: Stationarity and Transformations • Strict vs. weak stationarity • Transformations to achieve stationarity (differencing, log, seasonal adjustment) • White noise and random walks • Assignment: Determine stationarity with plots and transformations - -
4 Week 4: Unit Root Tests • Dickey-Fuller and Augmented Dickey-Fuller (ADF) tests • Phillips-Perron and KPSS tests • Structural breaks and the problem of spurious regression • Lab: Unit root testing on macroeconomic or financial data - -
5 Week 5: AR, MA, and ARMA Models • AR(p) and MA(q) processes • Conditions for stationarity and invertibility • Model selection using AIC, BIC • Hands-on: Estimating ARMA models using real data - -
6 Week 6: ARIMA and SARIMA Models • Integrating ARMA with differencing › ARIMA(p,d,q) • Seasonal components: SARIMA(p,d,q)(P,D,Q)[s] • Box-Jenkins methodology • Assignment: Fit ARIMA/SARIMA model to a macroeconomic indicator - -
7 Week 7: Model Diagnostics and Forecasting • Residual diagnostics (ACF, Ljung-Box, Q-statistic) • Out-of-sample forecasting • Forecast accuracy metrics: MSE, RMSE, MAE • Lab: Model fitting and forecast evaluation - -
8 Week 8: Midterm + Practical Review Session • In-class or take-home Midterm Exam • Optional Review Lab: Model implementation challenge with feedback - -
9 Week 9: Vector Autoregression (VAR) • When and why to use VAR • Impulse response functions (IRF) • Variance decomposition • Application: Analyzing economic indicators (e.g., GDP and inflation) - -
10 Week 10: Cointegration and Error Correction Models • Engle-Granger and Johansen tests • Long-run equilibrium and short-run dynamics • Error correction mechanism (ECM) • Lab: Cointegration tests using economic datasets - -
11 Week 11: Granger Causality and Structural VARs • Granger causality testing: definition and limitations • SVAR identification strategies • Assignment: Apply Granger causality to real-world economic or financial data - -
12 Week 12: Time-Varying Volatility: ARCH and GARCH Models • Heteroskedasticity in time series • ARCH(q), GARCH(p,q) models • Applications in finance and forecasting volatility • Lab: Estimating GARCH models with asset returns - -
13 Week 13: Advanced Topics and Model Selection • Structural breaks and regime-switching models • Model comparison and robustness checks • Generalized Method of Moments (GMM) introduction • Assignment: Research project outline and model proposal - -
14 Week 14: Student Project Presentations + Course Wrap-Up • Final presentations of empirical time series analysis projects • Peer and instructor feedback • Synthesis of theoretical and applied components • Final review and Q&A - -

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10
All 5 5 3 2 5 5 2
C1 5 5 3 2 5 5
C2 5 5 3 2 5 5
C3 5 5 2 2 5 5 2
C4 5 5 5 2 5 5 3
C5 5 2 5 1 5 5 3

  Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant

  
  https://sis.agu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=76784&lang=en
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