COURSE UNIT TITLE

: ADVANCED TIME SERIES ANALYSIS

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
STA 6054 ADVANCED TIME SERIES ANALYSIS ELECTIVE 3 0 0 8

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

PROFESSOR DOCTOR ESIN FIRUZAN

Offered to

Statistics (English)
STATISTICS (ENGLISH)
Statistics (English)

Course Objective

Advanced time series analysis provides the information about advanced statistical techniques and models used to study time series data in various research areas. The course includes univariate stationary and non-stationary models, vector autoregressive, models for estimation and inference in time series.

Learning Outcomes of the Course Unit

1   To understand the unit-root Stationarity/nonstationarity,
2   To get some information about the linear and nonlinear time series processes,
3   Understanding the cointegrated autoregressive processes,
4   To be able to apply multivariate VAR models,
5   To understand the multivariate volatility models.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Unit Root Nonstationarity (Random Walk,Random Walk with Drift)
2 Unit Root Nonstationarity (Trend-Stationary Time Series, General Unit-Root Nonstationary Models)
3 Continuous-Time Stochastic Processes (Wiener Process, Generalized Wiener Process)
4 Weak Stationarity and Cross-Correlation Matrices(Cross-correlation matrices, Linear Dependence, Multivariate Portmanteau Tests)
5 Vector Autoregressive Models (Reduced and Structural Forms, Stationarity Condition and Moments of a VAR(1) model, Vector AR(p) Models)
6 Unit Root Nonstationarity and Cointegration (An Error Correction Form, Specification of the Deterministic Function), Homework 1
7 Cointegrated VAR Models (Cointegration Test, Forecasting of Cointegrated VAR Models)
8 Threshold Cointegration (Multivariate Threshold Model, Estimation)
9 Multivariate Volatility Models and Their Applications (Exponentially Weighted Estimate, Some Multivariate GARCH Models), Homework 2
10 Multivariate Volatility Models and Their Applications (Reparameterization, Higher Dimensional Volatility)
11 State-Space Models and Kalman Filter (Local Trend Model, Linear State-Space Models)
12 State-Space Models and Kalman Filter (Model Transformation, Kalman Filter and Smoothing, Homework 3
13 Homework Evaluation
14 Homework Evaluation

Recomended or Required Reading

Textbook(s): Tsay, P.J., Davis, R.A. (2002), Analysis of Financial Time Series, Wiley.
Supplementary Book(s):
Lütkepohl H.2006, New Introduction to Multiple Time Series Analysis, Springer
White, H. (2002) Asymptotic Theory for Econometricians. (Revised Edition) San Diego: Academic Press.
Wei, W.W., (1990) Time Series Analysis- Univariate and Multivariate Methods, Wesley

Planned Learning Activities and Teaching Methods

Lecture, homework assignments, problem solving

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 ASG 1 ASSIGNMENT 1
2 ASG 2 ASSIGNMENT 2
3 ASG 3 ASSIGNMENT 3
4 FIN FINAL EXAM
5 FCG FINAL COURSE GRADE ASG 1 + ASG 2 + ASG 3/3 * 0.40 + FIN * 0.60
6 RST RESIT
7 FCGR FINAL COURSE GRADE (RESIT) ASG 1 + ASG 2 + ASG 3/3 * 0.40 + RST * 0.60


Further Notes About Assessment Methods

None

Assessment Criteria

Evaluation of homework assignments and final exam

Language of Instruction

English

Course Policies and Rules

Attendance to at least 70% for the lectures is an essential requirement of this course and is the responsibility of the student. It is necessary that attendance to the lecture and homework delivery must be on time. Any unethical behavior that occurs either in presentations or in exams will be dealt with as outlined in school policy. You can find the undergraduate policy at http://web.deu.edu.tr/fen

Contact Details for the Lecturer(s)

DEU Fen Fakültesi Istatistik Bölümü
e-posta: esin.firuzan@deu.edu.tr
Tel: 0232 301 85 57

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Preparations before/after weekly lectures 13 3 39
Preparation for final exam 1 36 36
Preparing assignments 3 25 75
Final 1 2 2
TOTAL WORKLOAD (hours) 194

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.1555555
LO.255555
LO.355555
LO.4555555
LO.55555

CONTACT INFORMATION
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Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

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