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Information
| Unit | INSTITUTE OF SOCIAL SCIENCES |
| ECONOMETRICS (MASTER) (WITH THESIS) | |
| Code | IEM709 |
| Name | Time Series Analysis I |
| Term | 2024-2025 Academic Year |
| Term | Fall and Spring |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Yüksek Lisans 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.
|
Course Goal / Objective
The aim of this course is to give the students a good theoretical and empirical understanding of statistical methods used in univariate time series analysis.
Course Content
The course content includes stochastic process and time series concepts; objectives of time and frequency based analyses; components of economic time series; difference equations and their stability; impulse-response function; expected values of processes, stationarity and ergodicity; trend and difference stationary processes; white noise process, MA(q), AR(p), random walk and ARIMA(p,d,q) processes; reversibility in MA(q) processes; over-parameterization problem in ARMA models; Box-Jenkins approach in ARIMA modeling; autocorrelation and partial autocorrelation functions of AR, MA and ARMA processes; unit root tests: ADF, PP, KPSS, HEGY tests; autoregressive conditional heteroscedasticity models: ARCH, GARCH, TARCH, EGARCH, IGARCH, ARCH-M models; regimented autoregressive models: TAR, SETAR, ESTAR, LSTAR models and their applications.
Course Precondition
None
Resources
James Douglas Hamilton, (1994) Time Series Analysis, Princeton University Press, ISBN: 9780691042893 Gebhard Kirchgässner, Jürgen Wolters (2007), Introduction to Modern Time Series Analysis, Springer, ISBN: 978-3-540-73291-4 Burak Güriş (2020) R Uygulamalı Doğrusal Olmayan Zaman Serileri Analizi, Der Yayınları ISBN: 978-975-353-628-8
Notes
Gebhard Kirchgässner, Jürgen Wolters (2007), Introduction to Modern Time Series Analysis, Springer, ISBN: 978-3-540-73291-4
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Defines the concept of stochastic process. |
| LO02 | Defines the concept of time series. |
| LO03 | List the components of economic time series. |
| LO04 | Solves the difference equations by recursive substitution. |
| LO05 | Determines whether a difference equation is stable and calculates its dynamic factors. |
| LO06 | Expresses the lags of the variables with the lag operator. |
| LO07 | It uses the difference processor, recognizes the white noise process, and recognizes ARMA processes. |
| LO08 | Recognizes the random walk process. |
| LO09 | Calculates the mean, autocovariances, autocorrelations and autocovariances of a process. |
| LO10 | Tests the stationarity of a series, Models a time series with the Box-Jenkins approach, Models conditional variance with the ARCH family of models. |
| LO11 | Selects the most appropriate regime-changing AR model for a time series. |
| LO12 | Tests whether there is a regime change in an AR model. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Explains contemporary concepts about Econometrics, Statistics, and Operation Research | 5 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Explains relationships between acquired knowledge about Econometrics, Statistics, and Operation Research | 5 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Explains how to apply acquired knowledge in the field to Economics, Business, and other social sciences | 5 |
| PLO04 | Beceriler - Bilişsel, Uygulamalı | Performs conceptual analysis to develop solutions to problems | 5 |
| PLO05 | Beceriler - Bilişsel, Uygulamalı | Models problems with Mathematics, Statistics, and Econometrics | 5 |
| PLO06 | Beceriler - Bilişsel, Uygulamalı | Interprets the results obtained from the most appropriate method to predict the model | 5 |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Synthesizes the information obtained by using different sources within the framework of academic rules in a field of research | 3 |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Uses acquired knowledge in the field to determine the vision, aim, and goals for an organization/institution | 3 |
| PLO09 | Beceriler - Bilişsel, Uygulamalı | Searches for new approaches and methods to solve problems being faced | 3 |
| PLO10 | Beceriler - Bilişsel, Uygulamalı | Presents analysis results conveniently | 3 |
| PLO11 | Beceriler - Bilişsel, Uygulamalı | Collects/analyzes data in a purposeful way | 5 |
| PLO12 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Converts its findings into a master's thesis or a professional report in Turkish or a foreign language | 3 |
| PLO13 | Beceriler - Bilişsel, Uygulamalı | Develops solutions for organizations using Econometrics, Statistics, and Operation Research | 3 |
| PLO14 | Beceriler - Bilişsel, Uygulamalı | Uses a package program/writes a new code for Econometrics, Statistics, and Operation Research | |
| PLO15 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Performs an individual work to solve a problem with Econometrics, Statistics, and Operation Research | 5 |
| PLO16 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Leads by taking responsibility individually and/or within the team | |
| 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 | Interprets the feelings, thoughts and behaviors of the related persons correctly/expresses himself/herself correctly in written and verbal form | |
| PLO19 | Yetkinlikler - Alana Özgü Yetkinlik | Interprets data on economic and social events by following current issues | 3 |
| PLO20 | Yetkinlikler - Alana Özgü Yetkinlik | Applies social, scientific and professional ethical values |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Stochastic process and time series concepts. Analysis of time series: time series analysis in time domain, time series analysis in frequency domain. Components of economic time series. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Problem Çözme |
| 2 | First-order difference equations: Definition, Solving a difference equation by recursive substitution, stability of first-order difference equations, Impulse-response functions of first-order difference equations. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Problem Çözme |
| 3 | pth-order difference equations: Definition, Solving a pth-order difference equation by recursive substitution, stability of pth-order difference equations, Impulse-response functions of pth-order difference equations. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Problem Çözme, Alıştırma ve Uygulama |
| 4 | Lag operator, Differencing operator. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Soru-Cevap, Anlatım, Problem Çözme |
| 5 | Expectations of processes, stationarity, and ergodicity. Trend stationary and difference stationary processes. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Problem Çözme |
| 6 | White noise process, MA(q) processes. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 7 | AR(p) processes, Random walk process, ARIMA(p,d,q) processes. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Problem Çözme |
| 8 | Mid-Term Exam | Preparing for the midterm exam | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Invertibility for MA(q) processes. Overparametrization of the ARMA models. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Problem Çözme, Tartışma |
| 10 | The Box-Jenkins method of ARIMA model identification. Autocorrelation and partian autocorrelation functions of AR, MA, and ARMA processes. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Problem Çözme |
| 11 | Autoregressive Conditional Heteroskedasticity Models: ARCH and GARCH models. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Problem Çözme |
| 12 | Autoregressive Conditional Heteroskedasticity Models: TARCH, EGARCH models. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Problem Çözme, Gösterip Yaptırma |
| 13 | Autoregressive Conditional Heteroskedasticity Models: IGARCH, ARCH-M models. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Problem Çözme |
| 14 | Autoregressive Regime-switching models: TAR, SETAR, ESTAR, LSTAR models. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Problem Çözme |
| 15 | Autoregressive Regime-switching models: Choosing the most appropriate model. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Problem Çözme |
| 16 | Term Exams | Final exam preparation | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Final exam preparation | Ölçme Yöntemleri: Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 3 | 42 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 5 | 70 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 15 | 15 |
| Final Exam | 1 | 30 | 30 |
| Total Workload (Hour) | 157 | ||
| Total Workload / 25 (h) | 6,28 | ||
| ECTS | 6 ECTS | ||