COURSE UNIT TITLE

: FINANCIAL ECONOMETRICS

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
EFE 7004 FINANCIAL ECONOMETRICS COMPULSORY 3 0 0 8

Offered By

Master`s Degree in Financial Economics (Non-Thesis-Evening)

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

PROFESSOR DOCTOR SAADET KASMAN

Offered to

Master`s Degree in Financial Economics (Non-Thesis-Evening)

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.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

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 Final

Recomended or Required Reading

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 STT TERM WORK (SEMESTER)
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE MTE * 0.40 + STT * 0.20 + FIN * 0.40
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.40 + STT * 0.20 + RST * 0.40


Further Notes About Assessment Methods

None

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.

Language of Instruction

English

Course Policies and Rules

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

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 12 3 36
Tutorials 12 1 12
Preparations before/after weekly lectures 12 3 36
Preparation for midterm exam 1 20 20
Preparation for final exam 1 30 30
Preparing assignments 1 50 50
Final 1 2 2
Midterm 1 2 2
TOTAL WORKLOAD (hours) 188

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.135
LO.24
LO.3445
LO.454
LO.5455