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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS EKO 6148 NONLINEAR TIME SERIES ANALYSIS ELECTIVE 3 0 0 6

Offered By

Econometrics

Level of Course Unit

Third Cycle Programmes (Doctorate Degree)

Course Coordinator

ASSOCIATE PROFESSOR MURAT TANIK

Offered to

Econometrics

Course Objective

Data acquisition, classification and properties of functions used to determine the Nonlinear Time-series (NLTS). To learn the use of models for the system to be controlled and predicted.

Learning Outcomes of the Course Unit

1   To be able to explain the features of NLTS data. 2   To be able to read NLTS graphs. 3   To be able to interpret various functions of NLTS data. 4   To be able to model NLTS data mathematically. 5   To be able to make predictions based on NLTS data. 6   To be able to measure the reliability of NLTS analyses.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Notation and terminology 2 Classification of NLTS data 3 ARCH model 4 Threshold model 5 Non parametric autoregressive model 6 Local linear modelling 7 Spline approximation 8 Mid-term 9 Threshold autoregressive model 10 GARCH model 11 Asymptothic properties of estimators 12 MLE and Bootstrap 13 Test of the ARCH effect 14 Stochastic volatility models

Recomended or Required Reading

Nonlinear time series analysis / Holger Kantz and Thomas Schreiber. New York : Cambridge University Press, 2002.

Planned Learning Activities and Teaching Methods

This course will be presented using methods of expression, discussion and solving problem.

Assessment Methods

Successful / Unsuccessful *** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

To be announced.

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 13 3 39 Preparations before/after weekly lectures 13 4 52 Preparation for midterm exam 1 35 35 Preparation for final exam 1 35 35 Midterm 1 3 3 Final 1 3 3 TOTAL WORKLOAD (hours) 167

Contribution of Learning Outcomes to Programme Outcomes

PO/LO PO.1 PO.2 PO.3 PO.4 PO.5 PO.6 PO.7 PO.8 PO.9 LO.1 2 2 2 2 2 2 2 2 2 LO.2 2 2 2 2 2 2 2 2 2 LO.3 2 2 2 2 2 2 2 2 2 LO.4 2 2 2 2 2 2 2 2 2 LO.5 2 2 2 2 2 2 2 2 2 LO.6 2 2 2 2 2 2 2 2 2