Time Series Analysis (ECON418) | GSÜ - ECTS Information Guide

Content

Objective

This course aims to equip students with the fundamental theoretical and applied tools needed to understand, model, and forecast the statistical properties of data observed over time (time series). By introducing key concepts such as stationarity, autocorrelation, and ARIMA models, the course provides students with an analytical foundation for analyzing economic, financial, and other social science data.

Course Content

The course covers the fundamental components of time series (trend, seasonality, cyclicality) and the concept of stationarity, followed by the model identification process using autocorrelation and partial autocorrelation functions. It addresses the construction, estimation, and diagnostic checking of autoregressive (AR), moving average (MA), ARMA, and ARIMA models within the Box-Jenkins methodology. SARIMA models for seasonal series, forecasting methods, and the evaluation of forecast performance are also covered. In its later parts, the course provides an introductory treatment of conditional heteroskedasticity (ARCH/GARCH models) as well as cointegration and error correction models (VECM). All topics are supported by hands-on applications using statistical software (EViews, R, or Python).

Course Learning Outcomes

Identify the fundamental properties of time series data (trend, seasonality, stationarity)
Understand the concept of stationarity and apply unit root tests (Dickey-Fuller, Augmented Dickey-Fuller)
Interpret autocorrelation and partial autocorrelation functions to determine the appropriate model order
Construct and estimate AR, MA, ARMA, and ARIMA models
Assess model adequacy through residual analysis
Model seasonal time series using methods such as SARIMA
Generate forecasts from time series models and evaluate forecast performance
Apply time series analysis using statistical software (EViews, R, Python, etc.)

Teaching and Learning Methods

Instruction combines theoretical lectures with hands-on computer lab sessions.

Lectures: presentation of the mathematical/statistical foundations of concepts and methods
Lab/application sessions: model building and forecasting on real datasets using software (EViews, R, or Python)
Weekly homework: independent application of the methods covered
Term project: end-to-end analysis of a time series chosen by the student
Office hours: individual consultation
Assessment: midterm exam, homework/project, and final exam

References

Box, G. E. P., Jenkins, G. M., Reinsel, G. C. — Time Series Analysis: Forecasting and Control, Wiley
Hamilton, J. D. — Time Series Analysis, Princeton University Press
Enders, W. — Applied Econometric Time Series, Wiley
Brockwell, P. J., Davis, R. A. — Introduction to Time Series and Forecasting, Springer
Wooldridge, J. M. — Introductory Econometrics: A Modern Approach (time series chapters), Cengage

Weekly Contents

Theory Topics
Week	Weekly Contents
1	Introduction to time series: basic concepts, components (trend, seasonality, cycle, random component)
2	The concept of stationarity; weak and strong stationarity
3	Autocorrelation function (ACF) and partial autocorrelation function (PACF)
4	Unit root tests: Dickey-Fuller and Augmented Dickey-Fuller tests
5	Autoregressive (AR) models
6	Moving average (MA) models
7	ARMA models
8	ARIMA models and the Box-Jenkins methodology
9	Midterm exam and model diagnostics (residual analysis, Ljung-Box test)
10	Seasonal ARIMA (SARIMA) models
11	Forecasting methods and evaluation of forecast performance
12	Conditional heteroskedasticity: introduction to ARCH and GARCH models
13	Introduction to cointegration and error correction models (VECM)
14	Applied case studies

Assessment System

Contribution to Overall Grade
Activities	Number	Contribution
Contribution of in-term studies to overall grade	2	65
Contribution of final exam to overall grade	1	35
Total	3	100

In-Term Studies
Activities	Number	Contribution
Assignments	0	0
Presentation	0	0
Midterm Examinations (including preparation)	0	0
Project	0	0
Laboratory	0	0
Other Applications	0	0
Quiz	0	0
Term Paper/ Project	0	0
Portfolio Study	0	0
Reports	0	0
Learning Diary	0	0
Thesis/ Project	0	0
Seminar	0	0
Other	0	0
Make-up	0	0
Total	0	0

Relation of Proficiency

No	Program Learning Outcomes	Contribution
No	Program Learning Outcomes	1	2	3	4	5
1	Demonstrate a basic knowledge of different economic theories and main discussions driving current theoretical approaches				X
2	Demonstrate ability to use basic qualitative and quantitative methods to asses different economic questions of applied fields					X
3	Show a sound understanding of current economic discussions and problems					X
4	Have an effective and clear communication in written and oral formats in different public and professional contexts.				X
5	Demonstrate basic knowledge of the world economic history and a sound knowledge of the Turkish economic history					X
7	Demonstrate knowledge of standard quantitative techniques and empirical models used in economics				X
8	Show ability to apply basic economic theory to an applied topic				X
9	Show a basic understanding of analytical methods, both theory- and model-based					X
10	Show ability to use economic concepts to discuss current economic issues				X
11	Reason logically and work analytically				X
12	Show awareness on social aspects of the economics in different applied and theoretical fields				X
13	Identify appropriate economic models to analyse problems				X
16	Show understanding of basic statistical and econometric concepts and ability to apply basic concepts to own empirical work				X
17	Demonstrate a sound knowledge of the current economic issues in Turkey and a general knowledge of the issues in the world economy					X

ECTS

Activities	Number	Period	Total Workload
Class Hours	14	3	42
Working Hours out of Class	14	2	28
Assignments	1	0	0
Presentation	0	0	0
Midterm Examinations (including preparation)	2	30	60
Project	0	0	0
Laboratory	0	0	0
Other Applications	2	10	20
Final Examinations (including preparation)	0	0	0
Quiz	2	0	0
Term Paper/ Project	0	0	0
Portfolio Study	0	0	0
Reports	0	0	0
Learning Diary	0	0	0
Thesis/ Project	0	0	0
Seminar	0	0	0
Other	0	0	0
Make-up	0	0	0
Yıl Sonu	0	0	0
Hazırlık Yıl Sonu	0	0	0
Hazırlık Bütünleme	0	0	0
Total Workload			150
Total Workload / 25			6.00
Credits ECTS			6