FACULTY OF ARTS AND SCIENCES

Department of Mathematics

ECON 407 | Course Introduction and Application Information

Course Name
Applied Economic Topics
Code
Semester
Theory
(hour/week)
Application/Lab
(hour/week)
Local Credits
ECTS
ECON 407
Fall/Spring
3
0
3
6

Prerequisites
  ECON 101 To succeed (To get a grade of at least DD)
or ECON 102 To succeed (To get a grade of at least DD)
or ECON 100 To succeed (To get a grade of at least DD)
or GEEC 203 To succeed (To get a grade of at least DD)
Course Language
English
Course Type
Elective
Course Level
First Cycle
Mode of Delivery -
Teaching Methods and Techniques of the Course -
Course Coordinator -
Course Lecturer(s)
Assistant(s) -
Course Objectives This course designed to help students move from the traditional and comparative static analysis of economic models to a modern and dynamic computational study. The ability to equate an economic problem, to formulate it into a mathematical model and to solve it computationally is becoming a crucial and distinctive competence for most economists. Therefore this course is organised around static and dynamic models, covering both macro- and microeconomic topics, exploring the numerical techniques required to solve those models. A key aim of the course is to enable students to develop the ability to modify the models themselves so that, using the MATLAB/Dynare codes, they can demonstrate a complete understanding of computational methods. This course aims to equip the student with powerful tools to write a microeconomic and macroeconomic model, to define an equilibrium, to approximate the equilibrium using computational methods, and calibrate and simulate the model so that the model can be used to answer economic questions.
Learning Outcomes The students who succeeded in this course;
  • Will be able to build an economic model.
  • Will be able to define and explain equilibrium in economics.
  • Will be able to calculate steady states.
  • Will be able to distinguish between stochastic and deterministic shocks.
  • Will be able to calibrate and simulate a simple macroeconomic model.
Course Description The course starts with an introduction to quantitative economics. We then discuss the benchmark deterministic model and competitive equilibrium. We then discuss steady state. The course continues with introduction to Matlab and Dynare programs. It concludes with the discussion of calibration and simulation of a simple real business cycle (RBC) model.

 



Course Category

Core Courses
Major Area Courses
Supportive Courses
X
Media and Management Skills Courses
Transferable Skill Courses

 

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week Subjects Related Preparation
1 Introduction to Matlab: Basic Commands Etter Chp 1-3
2 Introduction to Matlab; Functions and Graphics Etter Chp 4 Attaway12
3 Introduction to Matlab; Programming Etter Chp 7 Attaway 6
4 Economic Models and Matlab Sheppard
5 Supply Demand Model and Matlab: Computations A&V: Chp 1
6 Foreign Trade and Market Model: Welfare Analysis I A&V: Chp 2
7 Foreign Trade and Market Model: Welfare Analysis II A&V: Chp 3
8 Midterm
9 IS–LM model and Matlab Program A&V :Chp 4
10 Open Economy and Mundel-Fleming modeli: Matlab Program A&V :Chp 5
11 AD–AS model: Matlab Program A&V :Chp 6
12 Econometrics and Matlab Frain, Sheppard
13 Midterm
14 Microeconomic Analysis: Utility Maximization and Matlab Wang
15 Mikroekonomik Analiz: Profit Maximization and Matlab Wang
16 Project Presentations

 

Course Notes/Textbooks

S. Attaway: MATLAB A Practical Introduction to Programming and Problem Solving (2019)

Afonso and Vasconcelos: Computational Economics: A concise introduction (2016)

D. Etter: Introduction to MATLAB (2015)

Suggested Readings/Materials

Junior Understanding DSGE Models_ Theory and Applications-Vernon Press (2016)

Torres Introduction to Dynamic Macroeconomic General Equilibrium Models-Vernon Press (2016)

Fan Wang: Introductory-Mathematics-for-Economists-with-Matlab (2020)

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
16
10
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
2
20
Presentation / Jury
Project
Seminar / Workshop
Oral Exams
Midterm
2
40
Final Exam
1
30
Total

Weighting of Semester Activities on the Final Grade
20
80
Weighting of End-of-Semester Activities on the Final Grade
1
30
Total

ECTS / WORKLOAD TABLE

Semester Activities Number Duration (Hours) Workload
Theoretical Course Hours
(Including exam week: 16 x total hours)
16
3
48
Laboratory / Application Hours
(Including exam week: '.16.' x total hours)
16
1
16
Study Hours Out of Class
16
1
16
Field Work
0
Quizzes / Studio Critiques
0
Portfolio
0
Homework / Assignments
2
10
20
Presentation / Jury
0
Project
0
Seminar / Workshop
0
Oral Exam
0
Midterms
2
25
50
Final Exam
1
30
30
    Total
180

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1

To be able to have a grasp of basic mathematics, applied mathematics or theories and applications of statistics.

2

To be able to use advanced theoretical and applied knowledge, interpret and evaluate data, define and analyze problems, develop solutions based on research and proofs by using acquired advanced knowledge and skills within the fields of mathematics or statistics.

3

To be able to apply mathematics or statistics in real life phenomena with interdisciplinary approach and discover their potentials.

X
4

To be able to evaluate the knowledge and skills acquired at an advanced level in the field with a critical approach and develop positive attitude towards lifelong learning.

5

To be able to share the ideas and solution proposals to problems on issues in the field with professionals, non-professionals.

6

To be able to take responsibility both as a team member or individual in order to solve unexpected complex problems faced within the implementations in the field, planning and managing activities towards the development of subordinates in the framework of a project.

7

To be able to use informatics and communication technologies with at least a minimum level of European Computer Driving License Advanced Level software knowledge.

8

To be able to act in accordance with social, scientific, cultural and ethical values on the stages of gathering, implementation and release of the results of data related to the field.

9

To be able to possess sufficient consciousness about the issues of universality of social rights, social justice, quality, cultural values and also environmental protection, worker's health and security.

10

To be able to connect concrete events and transfer solutions, collect data, analyze and interpret results using scientific methods and having a way of abstract thinking.

X
11

To be able to collect data in the areas of Mathematics or Statistics and communicate with colleagues in a foreign language.

12

To be able to speak a second foreign language at a medium level of fluency efficiently.

13

To be able to relate the knowledge accumulated throughout the human history to their field of expertise.

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest

 


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