ISB0010 Time Series Analysis II

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

Information
Goal, Content & Resources
Course Learning Outcomes
Relation with Program Learning Outcome
Week Plan
Student Workload - ECTS

Information

Code ISB0010
Name Time Series Analysis II
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


Course Goal

The objectives of this course are to do modelling and analysis of time series.

Course Content

Vector Autoregression, Bayesian Analysis, The Kalman Filter, Generalized Method of Moments, Processes with Deterministic Time Trends, Univariate Processes with Unit Roots, Unit Roots in Multivariate Time Series, Time Series Models of Heteroskedasticity

Course Precondition

no

Resources

1. Hamilton,J.D. (1994).Time series analysis. Princeton univ. press., NEW JERSEY. 2. Enders, W. (1995). Applied econometric time series. John Wiley and Sons,INC. 3. Akdi, Y.(2003). Zaman serileri analizi. Bıçaklar kitabevi. ANKARA. 4. Sevüktekin, M. Ve Nargeleçekenler, M.(2007). Ekonometrik zaman serileri analizi. Nobel kitabevi. ANKARA.

Notes

1. Hamilton,J.D. (1994).Time series analysis. Princeton univ. press., NEW JERSEY. 2. Enders, W. (1995). Applied econometric time series. John Wiley and Sons,INC. 3. Akdi, Y.(2003). Zaman serileri analizi. Bıçaklar kitabevi. ANKARA. 4. Sevüktekin, M. Ve Nargeleçekenler, M.(2007). Ekonometrik zaman serileri analizi. Nobel kitabevi. ANKARA.


Course Learning Outcomes

Order Course Learning Outcomes
LO01 Must learn autoregressive vectors
LO02 Bayesian analysis should be learned
LO03 Kalman Filter should understand the estimation method
LO04 Learn to generalize the method of moments
LO05 Understand deterministic time series processes
LO06 Comprehend univariate processes with unit roots
LO07 Must learn unit roots in multivariate time series models
LO08 Must learn heterosdastic time series models


Relation with Program Learning Outcome

Order Type Program Learning Outcomes Level
PLO01 Bilgi - Kuramsal, Olgusal Have in-depth theoretical and practical knowledge about Probability and Statistics 5
PLO02 Bilgi - Kuramsal, Olgusal They have the knowledge to make doctoral plans in the field of statistics.
PLO03 Bilgi - Kuramsal, Olgusal Has comprehensive knowledge about analysis and modeling methods used in statistics. 4
PLO04 Bilgi - Kuramsal, Olgusal Has comprehensive knowledge of methods used in statistics. 2
PLO05 Bilgi - Kuramsal, Olgusal Make scientific research on Mathematics, Probability and Statistics. 2
PLO06 Bilgi - Kuramsal, Olgusal Indicates statistical problems, develops methods to solve. 3
PLO07 Bilgi - Kuramsal, Olgusal Apply innovative methods to analyze statistical problems. 3
PLO08 Bilgi - Kuramsal, Olgusal Designs and applies the problems faced in the field of analytical modeling and experimental researches. 3
PLO09 Bilgi - Kuramsal, Olgusal Access to information and do research about the source.
PLO10 Bilgi - Kuramsal, Olgusal Develops solution approaches in complex situations and takes responsibility.
PLO11 Bilgi - Kuramsal, Olgusal Has the confidence to take responsibility.
PLO12 Beceriler - Bilişsel, Uygulamalı They demonstrate being aware of the new and developing practices. 2
PLO13 Beceriler - Bilişsel, Uygulamalı He/She constantly renews himself/herself in statistics and related fields.
PLO14 Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği Communicate in Turkish and English verbally and in writing.
PLO15 Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği Transmits the processes and results of their studies clearly in written and oral form in national and international environments.
PLO16 Yetkinlikler - Öğrenme Yetkinliği It considers the social, scientific and ethical values ​​in the collection, processing, use, interpretation and announcement stages of data and in all professional activities. 3
PLO17 Yetkinlikler - Öğrenme Yetkinliği Uses the hardware and software required for statistical applications. 3


Week Plan

Week Topic Preparation Methods
1 Autoregressive vectors source reading Öğretim Yöntemleri:
Anlatım
2 Maximum likelihood estimation of constrained autoregressive vectors source reading Öğretim Yöntemleri:
Anlatım
3 Bayesian analysis source reading Öğretim Yöntemleri:
Anlatım
4 Obtaining the Kalman Filter source reading Öğretim Yöntemleri:
Anlatım
5 Maximum likelihood estimates of parameters source reading Öğretim Yöntemleri:
Anlatım
6 Generalization of the method of moments (GMM) source reading Öğretim Yöntemleri:
Anlatım
7 Midterm Ölçme Yöntemleri:
Yazılı Sınav, Ödev
8 GMM and maximum likelihood estimate source reading Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Tartışma
9 Deterministic time series processes source reading Öğretim Yöntemleri:
Anlatım
10 Univariate processes with unit roots I source reading Öğretim Yöntemleri:
Anlatım
11 Univariate processes with unit roots II source reading Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Tartışma
12 Unit roots in multivariate time series models I source reading Öğretim Yöntemleri:
Anlatım
13 Unit roots in multivariate time series models II source reading Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Tartışma
14 Heterosdastic time series models I source reading Öğretim Yöntemleri:
Anlatım
15 Heterosdastic time series models II source reading Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Alıştırma ve Uygulama
16 final exam Ölçme Yöntemleri:
Yazılı Sınav, Ödev
17 final exam Ölçme Yöntemleri:
Yazılı Sınav, Ödev


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