# COURSE UNIT TITLE

: TIME SERIES ECONOMETRICS

#### Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
ECN 5036 TIME SERIES ECONOMETRICS ELECTIVE 3 0 0 6

#### Offered By

Economics (English)

#### Level of Course Unit

Second Cycle Programmes (Master's Degree)

#### Course Coordinator

ASSISTANT PROFESSOR ERDOST TORUN

#### Offered to

Economics (English)

#### Course Objective

The objective of the course is to skills for dealing with univariate and multivariate time series modeling of economic and financial data. Stationary and nonstationary time series with unit roots, AR, MA, ARMA and ARIMA models, cointegration, error correction models, VAR and causality are the main topics to be covered. The use of computer is an integrated part of the course. Students are expected to prepare a term project to demonstrate their skills developed in the course.

#### Learning Outcomes of the Course Unit

 1 Be able to collect raw data related to economic and financial, and make them ready for statistical and econometric analysis. 2 Demonstrate understanding of building time series models that describe the data generating process behind data. 3 Identify problems with existing time series models so that the learner could employ appropriate econometric tools to solve the problem. 4 Be able to interpret the estimation results so that the learner can draw implications from the results. 5 Demonstrate engaging an independent empirical research in order to prepare a tem project.

Face -to- Face

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#### Course Contents

 Week Subject Description 1 Math and Statistics Review 2 Time Series and their characteristics 3 Linear time series analysis and its application 4 Linear time series analysis and its application 5 Conditional heteroscedastic models 6 Conditional heteroscedastic models 7 Midterm 8 Conditional heteroscedastic models 9 Multivariate time series analysis and its applications 10 Multivariate time series analysis and its applications 11 Multivariate time series analysis and its applications 12 Multivariate time series analysis and its applications 13 Multivariate time series analysis and its applications 14 General Overview

1. Ruey S. Tsay Analysis of Financial Time Series, 2nd Ed. Willey, 2005
2. Lecture Notes

#### Planned Learning Activities and Teaching Methods

1. Lectures
2. Class Discussions
3. Term Project

#### Assessment Methods

 SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 MTE MIDTERM EXAM 2 FCG FINAL COURSE GRADE 3 FCGR FINAL COURSE GRADE MTE * 0.40 + FCG* 0.60 4 RST RESIT 5 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.40 + RST* 0.60

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

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#### Assessment Criteria

1. The learner will use necessary statistical and time series econometric tools to engage independent research.
2. The learner will clearly recognize the problems with existing econometric models.
3. The learner will build time series econometric models for estimation purposes.
4. The learner will interpret empirical results.
5. The learner will draw some policy implications from estimation results.

English

#### Course Policies and Rules

1. It is obligatory to attend at least 70% of the classes.

To be announced.

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