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| Unit | INSTITUTE OF SOCIAL SCIENCES |
| ECONOMETRICS (PhD) | |
| Code | IEM1845 |
| Name | Applied Time Series Models I |
| Term | 2025-2026 Academic Year |
| Term | Fall and Spring |
| Duration (T+A) | 4-0 (T-A) (17 Week) |
| ECTS | 8 ECTS |
| National Credit | 4 National Credit |
| Teaching Language | Türkçe |
| Level | Doktora Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Dr. Öğr. Üyesi FELA ÖZBEY |
| Course Instructor |
The current term course schedule has not been prepared yet. Previous term groups and teaching staff are shown.
|
Course Goal / Objective
The aim of this course is to introduce some advanced methods used in time series analysis, and their applications with R programming language.
Course Content
Introduction, Fundamental Concepts, Trends, Models For Stationary Time Series, Models For Nonstationary Time Series, Model Specification, Parameter Estimation, applications with R programming language.
Course Precondition
None
Resources
Robert H. Shumway, David S. Stoffer (2011),Time Series Analysis and its Applications with R Examples, Third Edition, Springer-Verlag, New York, ISBN 978-1-4419-7864-6
Notes
James Douglas Hamilton, (1994) Time Series Analysis, Princeton University Press, ISBN: 9780691042893
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Specifies time series relations. |
| LO02 | Chooses the most appropriate model for time series data. |
| LO03 | Estimates time series models. |
| LO04 | Codes techniques and models taught in this course. |
| LO05 | Uses R programming language fluently. |
| LO06 | Explains Parameter Estimations. |
| LO07 | Explains stationary time series models. |
| LO08 | Explains non-stationary time series models. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Identify an econometric problem and propose a new solution to it | 5 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Develops new knowledge using current concepts in Econometrics, Statistics and Operations Research | 3 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Explain for what purpose and how econometric methods are applied to other fields and disciplines | 3 |
| PLO04 | Beceriler - Bilişsel, Uygulamalı | Using her knowledge, brings original solutions to problems in Economics, Business Administration and other social sciences | 4 |
| PLO05 | Beceriler - Bilişsel, Uygulamalı | Creates a new model using mathematics, statistics and econometrics knowledge to solve the problem encountered | |
| PLO06 | Beceriler - Bilişsel, Uygulamalı | Interprets the results obtained from the most appropriate method to predict the model | 3 |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Performs conceptual analysis to develop solutions to problems | |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Collects data on purpose | 5 |
| PLO09 | Beceriler - Bilişsel, Uygulamalı | Synthesizes the information obtained by using different sources within the framework of academic rules in a field of research | 2 |
| PLO10 | Beceriler - Bilişsel, Uygulamalı | Presents analysis results conveniently | |
| PLO11 | Beceriler - Bilişsel, Uygulamalı | Converts its findings into a master's thesis or a professional report in Turkish or a foreign language | |
| PLO12 | Beceriler - Bilişsel, Uygulamalı | It researches current approaches and methods to solve the problems it encounters and proposes new solutions | 2 |
| PLO13 | Beceriler - Bilişsel, Uygulamalı | Develops long-term plans and strategies using econometric and statistical methods | 2 |
| PLO14 | Beceriler - Bilişsel, Uygulamalı | Uses a package program/writes a new code for Econometrics, Statistics, and Operation Research | 5 |
| PLO15 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Performs self-study using knowledge of Econometrics, Statistics and Operations to solve a problem | 2 |
| PLO16 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Leads the team by taking responsibility | |
| PLO17 | Yetkinlikler - Öğrenme Yetkinliği | Being aware of the necessity of lifelong learning, it constantly renews itself by following the current developments in the field of study | 3 |
| PLO18 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Uses acquired knowledge in the field to determine the vision, aim, and goals for an organization/institution | |
| PLO19 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Interprets the feelings, thoughts and behaviors of the related persons correctly/expresses himself/herself correctly in written and verbal form | |
| PLO20 | Yetkinlikler - Alana Özgü Yetkinlik | Applies social, scientific and professional ethical values | |
| PLO21 | Yetkinlikler - Alana Özgü Yetkinlik | Interprets data on economic and social events by following current issues | 3 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction: Examples of Time Series; A Model-Building Strategy; Time Series Plots in History; R applications. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Gösterip Yaptırma |
| 2 | Fundamental Concepts: Time Series and Stochastic Processes; Means, Variances, and Covariances; Stationarity; R applications. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Gösterip Yaptırma, Tartışma |
| 3 | Trends: Deterministic Versus Stochastic Trends; Estimation of a Constant Mean; Regression Methods; R applications. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Gösterip Yaptırma |
| 4 | Trends: Reliability and Efficiency of Regression Estimates; Interpreting Regression Output; Residual Analysis; R applications. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 5 | Models For Stationary Time Series: General Linear Processes; Moving Average Processes; Autoregressive Processes; R applications. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Gösterip Yaptırma |
| 6 | Models For Stationary Time Series: The Mixed Autoregressive Moving Average Model; Invertibility; The Autocorrelation Function for ARMA(p,q); R applications. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Gösterip Yaptırma |
| 7 | Models For Nonstationary Time Series: Stationarity Through Differencing; ARIMA Models; R applications. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Gösterip Yaptırma |
| 8 | Mid-Term Exam | Preparing for the midterm exam | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Models For Nonstationary Time Series: Constant Terms in ARIMA Models; Other Transformations; The Backshift Operator; R applications. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Gösterip Yaptırma |
| 10 | Model Specification: Properties of the Sample Autocorrelation Function; The Partial and Extended Autocorrelation Functions; Nonstationarity; R applications. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Gösterip Yaptırma, Alıştırma ve Uygulama |
| 11 | Model Specification: Specification of Some Simulated Time Series; Other Specification Methods; Specification of Some Actual Time Series; R applications. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Gösterip Yaptırma |
| 12 | Parameter Estimation: The Method of Moments; Least Squares Estimation; Maximum Likelihood and Unconditional Least Squares; R applications. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Gösterip Yaptırma |
| 13 | Parameter Estimation: Properties of the Estimates; Illustrations of Parameter Estimation; Bootstrapping ARIMA Models; R applications. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Gösterip Yaptırma |
| 14 | Model Diagnostics: Residual Analysis; Overfitting and Parameter Redundancy; R applications. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Gösterip Yaptırma |
| 15 | Applications on Some Actual Time Series. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Gösterip Yaptırma |
| 16 | Term Exams | Final exam preparation | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Final exam preparation | Ölçme Yöntemleri: Yazılı Sınav |
Assessment (Exam) Methods and Criteria
Current term shares have not yet been determined. Shares of the previous term are shown.
| Assessment Type | Midterm / Year Impact | End of Term / End of Year Impact |
|---|---|---|
| 1. Homework | 100 | 40 |
| General Assessment | ||
| Midterm / Year Total | 100 | 40 |
| 1. Final Exam | - | 60 |
| Grand Total | - | 100 |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 4 | 56 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 8 | 112 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 2 | 4 | 8 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 12 | 12 |
| Final Exam | 1 | 24 | 24 |
| Total Workload (Hour) | 212 | ||
| Total Workload / 25 (h) | 8,48 | ||
| ECTS | 8 ECTS | ||