ECON 324 | Course Introduction and Application Information - Department of Mathematics

Course Name

Applied Econometrics

Code	Semester	Theory (hour/week)	Application/Lab (hour/week)	Local Credits	ECTS
ECON 324	Fall/Spring	3	0	3	5

Prerequisites

ECON 301 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

-

National Occupation Classification

-

Course Coordinator

Prof. Dr. Ayla OĞUŞ BİNATLI

Course Lecturer(s)

Prof. Dr. Celal KÜÇÜKER

Assistant(s)

Araş. Gör. Samet ŞİMŞEK

Course Objectives	The main objective of the course is to teach students advanced econometric methods building on their knowledge of the classical regression model and its violations learned in ECON 301. Each student is required to prepare a project to show their skills developed in this course.
Learning Outcomes	The students who succeeded in this course; Will be able to select and apply appropriate statistical models pertaining to various subject areas in economics. Will be able to estimate nonlinear models. Will be able to analyse models with a dichotomy variable as dependent variable, specifically, the LPM, Logit, and Probit models. Will be able to use simultaneous equation models; the indirect and two step least squares methods; dynamic models with time delayed explanatory variables; and Granger causality test. Will be able to explain basic theories of stationary and nonstationary models such as the ARMA and the ARIMA models. Will be able to use models for panel data.
Course Description	The course will teach advanced techniques that are required for empirical work in economics. Emphasis will be on the use and interpretation of single equation and system estimation techniques rather than on their derivation. The purpose of the course is to help students understand how to interpret economic data and conduct empirical tests of economic theories. It will focus on issues that arise in using such data, and the methodology for solving these problems. Specific topics include limited dependent variables, simultaneous equations, time series models, nonstationarity and cointegration and panel data analysis. The regression package EVIEWS will be used for empirical work.
Related Sustainable Development Goals

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

Week	Subjects	Related Preparation
1	Introduction
2	Time Series Models	Using Econometrics: A Practical Guide Chapter 12
3	Time Series Models – cont’d.	Using Econometrics: A Practical Guide Chapter 12
4	Nonstationary Data	Using Econometrics: A Practical Guide Chapter 12
5	ARCH - GARCH Models	Course Notes and Applications
6	Dummy Dependent Variable Techniques	Using Econometrics: A Practical Guide Chapter 13
7	Midterm Exam
8	Simultaneous Equations	Using Econometrics: A Practical Guide Chapter 14
9	Simultaneous Equations – cont’d	Using Econometrics: A Practical Guide Chapter 14
10	Forecasting	Using Econometrics: A Practical Guide Chapter 15
11	Forecasting – cont’d	Using Econometrics: A Practical Guide Chapter 15
12	ARIMA Models	Using Econometrics: A Practical Guide Chapter 15
13	Analysis of Panel Data
14	Analysis of Panel Data
15	Review of the semester
16	Review of the semester

Course Notes/Textbooks	A. H. Studenmund, Using Econometrics: A Practical Guide (Fifth Edition)
Suggested Readings/Materials

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

Weighting of Semester Activities on the Final Grade	3	70
Weighting of End-of-Semester Activities on the Final Grade	1	30
Total

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		0
Study Hours Out of Class	16	2	32
Field Work			0
Quizzes / Studio Critiques			0
Portfolio			0
Homework / Assignments			0
Presentation / Jury			0
Project	2	15	30
Seminar / Workshop			0
Oral Exam			0
Midterms	1	20	20
Final Exam	1	20	20
		Total	150

#	Program Competencies/Outcomes	* Contribution Level
#	Program Competencies/Outcomes	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.	-	-	X	-	-
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.	-	-	-	-	-
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.	-	-	-	-	-