COURSE UNIT TITLE

: LINEAR TIME SERIES ANALYSIS

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
STA 5077 LINEAR TIME SERIES ANALYSIS ELECTIVE 3 0 0 7

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

PROFESSOR ESIN FIRUZAN

Offered to

Statistics
STATISTICS

Course Objective

The course provides a survey of the theory and application of time series methods. Topics covered will include univariate stationary and non-stationary models, models for estimation and inference in persistent time series.

Learning Outcomes of the Course Unit

1   To develop the skills needed to do empirical research in fields operating with time series data sets,
2   To obtain auto-covariance function of any stochastic process,
3   To identify Non-seasonal Box-Jenkins models using autocorrelation and partial autocorrelation function,
4   To estimate the parameters with maximum likelihood methods, Yule-walker estimation methods etc.
5   To make forecast using algorithm

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Some Zero-Mean Models
2 Models with Trend and Seasonality
3 Linear Processes
4 Properties of the Sample Mean and Autocorrelation Function
5 Forecasting Stationary Time Series
6 The ACF and PACF of an ARMA(p,q) Process, Preparing Individual Assignments
7 Forecasting ARMA Processes
8 Yule-Walker Estimation
9 Burg s Algorithm, Preparing Individual Assignments
10 The Innovations Algorithm
11 Maximum Likelihood Estimation
12 Diagnostic Checking
13 The FPE Criterion-The AICC Criterion, Preparing Individual Assignments
14 Forecasting

Recomended or Required Reading

Textbook(s): P. J. Brockwell and R. A. Davis, Introduction to Time Series and Forecasting, 2nd Edn, Prentice-Hall, 2003.
Supplementary Book(s): William W. S. Wei, Time Series Analysis-Univariate and Multivariate Methods, 2nd Edn, Pearson Education , 2006

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

If the instructor needs to add some explanation or further note, this column can be selected from the DEBIS menu.

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: 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 14 2 28
Preparation for final exam 1 36 36
Preparing assignments 3 19 57
Final 1 2 2
TOTAL WORKLOAD (hours) 165

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.155555
LO.255555
LO.355555
LO.455555
LO.5555

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

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