sap course 1429216376

Course Code & Number:

ECON 303

Course Title:

Mathematical Economics

Level of Course:

BS

Credits:

(3+0+0) 3 TEDU Credits, 6 ECTS Credits

Catalog Description:

Mathematics for advanced economic analysis. Optimization under inequality constraints. Dynamic economic models. Differential and difference equations and dynamic optimization. Optimal control theory.

Pre-requisites & Co-requisites:

Pre-requisites: MATH 102
Co-requisites: NONE
Year of Study: 
Junior
Semester: 
Fall
Mode of Delivery: 
Face-to-face
Language of Instruction: 
English
Course Type: 
Elective
Required Reading: 
1. Wainwright. K. and A. Chiang, Fundamental Methods of Mathematical Economics. 4th Edition, McGraw-Hill/Irwin, New York, 2004.
Course Objective: 

The main objective of this course is to provide mathematics for advanced economic analysis and to show how mathematical techniques may be applied to economic modelling. Students would understand the optimization under inequality constraints, dynamic economic models, differential and difference equations and optimal control theory.

Extended Description: 

Main Concepts Such as Vector Spaces and Matrix Algebra. Optimization in Dynamic and Static Models. Exponential and Logarithmic Functions. Analysis of the Case of More Than One Choice Variable in Optimization Models. Understanding Optimization with Equality Constraints and the Solution Methodology. Lagrange Multiplier.

Computer Usage: 
Students will use MS Office applications (Word, Excel, Access, Power point) to work on their weekly assignments about 2 hours a week.
Learning Outcomes: 

Upon succesful completion of this course, a student will be able to
1. Recall main concepts such as matrix algebra and vector spaces. 
2. Solve optimization problems under inequality constraints. 
3. Formulate economic problems mathematically. 
4. Apply mathematical techniques to economic problems in both dynamic and static settings.
5. Interpret mathematical formulations of economic problems
6. Analyze dynamic economic models. 
7. Solve difference and differential equations. 
8. Solve dynamic optimization models. 
9. Describe optimal control theory. 

Planned Learning Activities and Teaching Methods: 
Telling/Explaining
Discussion/Debate
Questioning
Reading
Peer Teaching
Demonstrating
Problem Solving
Case Study/Scenarion Analysis
Simulation & Games
Video Presentations
Oral Presentation
Guest Speakers
Web Searching
Assessment Methods and Criteria: 
Test / Exam
Quiz/Homework
Written Project
Presentation (Oral/Poster)

Student Workload:

Quizzes /Homeworks
40
hrs
Midterm Exam 1
16
hrs
Final Exam
16
hrs
Research Review
24
hrs

Prepared By:

Jülide Yıldırım Öcal

Revised By:

sap_editor