IEM709 Time Series Analysis I

6 ECTS - 3-0 Duration (T+A)- . Semester- 3 National Credit

Information

Code IEM709
Name Time Series Analysis I
Term 2023-2024 Academic Year
Semester . Semester
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 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

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. Difference equations: Stability of difference equations, Impulse-response function. Expectations of processes, stationarity, and ergodicity. Trend stationary and difference stationary processes. White noise process, MA(q) processes, AR(p) processes, Random walk process, ARIMA(p,d,q) processes. Invertibility for MA(q) processes. Overparametrization of the ARMA models. The Box-Jenkins method of ARIMA model identification. Autocorrelation and partian autocorrelation functions of AR, MA, and ARMA processes. Unit root tests: ADF, PP, KPSS, HEGY tests. Autoregressive Conditional Heteroskedasticity Models: ARCH, GARCH, TARCH, GARCH, IGARCH, ARCH-M models. Autoregressive Regime-switching models: TAR, SETAR, ESTAR, LSTAR models.

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.
LO06 Calculates the dynamic multipliers of a difference equation.
LO07 Expresses the lags of the variables with the lag operator.
LO08 Uses the differencing operator.
LO09 Recognizes the white noise process.
LO10 Recognizes ARMA processes.
LO11 Recognizes the random walk process.
LO12 Calculates the mean of a process.
LO13 Calculates the autocovariances of a process.
LO14 Calculates the autocorrealtions of a process.
LO15 Calculates the partial autocorrealtions of a process.
LO16 Tests the stationarity of a series.
LO17 Models a time series by using the Box-Jenkins approach.
LO18 Models conditional variance with ARCH family models.
LO19 Tests whether there is a regime-switching an AR model.
LO20 Chooses the most appropriate Autoregressive Regime-switching model for a time series.


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 that does not 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 Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği Performs an individual work to solve a problem with Econometrics, Statistics, and Operation Research 5
PLO15 Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği Leads by taking responsibility individually and/or within the team
PLO16 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
PLO17 Yetkinlikler - İletişim ve Sosyal Yetkinlik Uses a package program of Econometrics, Statistics, and Operation Research or writes a new code
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
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, Problem Çözme
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 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
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
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 Ölçme Yöntemleri:
Yazılı Sınav
17 Term Exams 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

Update Time: 11.05.2023 04:29