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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS EMT 4032 NONLINEAR TIME SERIES ANALYSIS ELECTIVE 3 0 0 5

Offered By

Econometrics

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSOCIATE PROFESSOR ÖZLEM KIREN GÜRLER

Offered to

Econometrics Econometrics (Evening)

Course Objective

Learning Outcomes of the Course Unit

1   To be able to grasp the basic concepts of nonlinearity 2   To be able to use R and libraries 3   To be able to develop different nonlinear time series models 4   To be able to apply data pre-processing and model evaluation skills 5   To be able to analyse real-world data and conduct independent research 6   To be able to develop methodological and practical skills for conducting empirical nonlinear time series analysis

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Why is nonlinearity important Simple Concepts, Examples of Nonlinear Time Series Conditional Mean Models 2: Threshold and Smooth Transition Models 2 Identification of Nonlinear Time Series: Nonlinearity tests Conditional Mean Models 3: Model Specification and Estimation 3 Univariate Parametric Nonlinear Models 1: A Two-regime TAR Model with R applications The Concept of Forecasting: Provide forecasts by using nonlinear models 4 Univariate Parametric Nonlinear Models 2: Markov Switching Models with R applications Real world applications in finance with R program 5 Univariate Parametric Nonlinear Models 3: Real world data applications and determine the best appropriate model 6 Basic Nonlinear Models 1: The structures of ARCH and GARCH Models 7 Advanced GARCH Models 1: EGARCH, TGARCH and the other asymmetric GARCH models 8 Advanced GARCH Models 2: Volatility Modelling and Forecasting 9 Advanced GARCH Models 3: Comparison of the GARCH family models 10 Conditional Mean Models 1: Nonlinear Conditional Mean Models (SETAR, STAR)

Recomended or Required Reading

NONLINEAR TIME SERIES ANALYSIS, Yazrlar: Ruey S. Tsay, University of Chicago, Chicago, Illinois, United States Rong Chen, Rutgers, The State University of New Jersey,United States

Planned Learning Activities and Teaching Methods

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 STT TERM WORK (SEMESTER) 2 MTE MIDTERM EXAM 3 MTEG MIDTERM GRADE STT * 0.50 +MTE * 0.50 4 FIN FINAL EXAM 5 FCG FINAL COURSE GRADE MTEG * 0.40 + FIN * 0.60 6 RST RESIT 7 FCGR FINAL COURSE GRADE (RESIT) MTEG * 0.40 + RST * 0.60

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)

emrah.gulay@deu.edu.tr

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 14 3 42 Preparation for final exam 1 15 15 Individual homework preperation (CBIKO Talent Gate) 1 25 25 Preparing presentations 1 11 11 Preparations before/after weekly lectures 14 2 28 Final 1 2 2 Midterm 1 2 2 TOTAL WORKLOAD (hours) 125

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 PO.10 LO.1 1 LO.2 1 LO.3 1 LO.4 1 LO.5 1 LO.6 1