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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS EKO 6053 STOCHASTICAL PROCESS ELECTIVE 3 0 0 6

Offered By

Econometrics

Level of Course Unit

Third Cycle Programmes (Doctorate Degree)

Course Coordinator

ASSOCIATE PROFESSOR KADIR ERTAŞ

Offered to

Econometrics

Course Objective

The main objective of the course is teach concepts of conditional probability and conditional expected value, to introduce Markov Chains and to show implementations of Markov Decision Processes in social science

Learning Outcomes of the Course Unit

1   To be able to make a practice of conditional probability and conditional expected value. 2   To be able to make a practice of business and economics related to compound random variables. 3   To be able to use Markov models for solution of problems in business, economics and marketing. 4   To be able to make a practice of Markov Decision Processes.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 The Theory of Conditional Probability and Conditional Expected Value 2 Implementations for discrete random variables and continuous random variables 3 Compound Random Variables and Its implementation 4 Compound Random Variables and Its implementation 5 Introduction to Markov Chains and It s base Theory 6 Sample Problems related to Markov Chains 7 Random Walk Model 8 Mid-term 9 Implementations for Random Walk Model 10 Chapman-Kolmogorov Equations 11 Implementations for Chapman-Kolmogorov Equations 12 Markov Decision Processes 13 Hidden Markov Chains 14 Hidden Markov Chains

Recomended or Required Reading

Introduction to Probability Models, Elsevier Inc., Sheldon Ross.

Planned Learning Activities and Teaching Methods

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

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.

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 3 39 Preparation for midterm exam 1 30 30 Preparation for final exam 1 30 30 Midterm 1 3 3 Final 1 3 3 TOTAL WORKLOAD (hours) 144

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 1 LO.2 1 LO.3 1 LO.4 1