ECONOMICS
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

Course Introduction and Application Information

Course Code	Course Name	Semester	Theoretical	Practical	Credit	ECTS
ECO4441	Microeconometrics	Fall	3	0	3	6

This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction:	English
Type of course:	Departmental Elective
Course Level:	Bachelor’s Degree (First Cycle)
Mode of Delivery:	Hybrid
Course Coordinator :	Dr. Öğr. Üyesi GÖKHAN ŞAHİN GÜNEŞ
Course Objectives:	The objective of this course is to investigate the microeconometric methods which are used to investigate the firm level and household level data sets and comment on models' results. During the semester, the students will understand the applied field of their theoretical knowledge by learning statistical packages (such as STATA and R)

Learning Outcomes

The students who have succeeded in this course;
1 Learn basic characteristics of microeconometrics and micro level data sets.
2 Will be able to establish dependent qualitative (discrete choice) models.
3 Will be able to select the most appropriate model.
4 Will be able to establish multivariate models and able to estimate and make inference on results.
5 Will be able to establish and estimate multinomial logit, ordered logit/probit models and comment on the resulths of the model..
6 Will be able to analyze panel data sets at basic level.
7 Learn endogeneity problem and propose solutions for it.
8 Learn selection bias problem and propose solutions for it.

Course Content

After reviewing the basic principles of econometrics and Ordinary Least Squares (OLS) methods, microeconometrics will be introduced. At that time, micro data sets and application fields of micro data sets will be taught. Linear Probability Models, Probit/Logit Models, Tobit models, multinomial logit models, ordered probit/logit models, endogeneity problem, instrumental variable approach, selection bias problem, propensity score matching techniques, regression discontinuity design, difference-in-difference methodology and panel data set analyses (fixed and random effects models) will be investigated during the semester. During the semester, the students will understand the applied field of their theoretical knowledge by learning statistical packages (such as STATA and R)

Weekly Detailed Course Contents

Week	Subject	Related Preparation
1)	Introduction, Basic Concepts, Ordinary Least Squares (OLS) methods	Chapter 1,4 Cameron
2)	Structure of Micro Level Data Set, Introduction to Cross-Section Data, Introduction to models for Cross-Section Data	Chapter 3 Cameron
3)	Linear Probability, Probit and Logit Models	Chapter 14, Cameron
4)	Ordered Probit/Logit and Multinomial Logit Models	Chapter 15 Cameron
5)	Tobit Models	Chapter 16 Cameron
6)	Endogeneity Problem in Cross-Section Data; Suggestions for the solution of endogeneity problem	Chapter 4 Cameron
7)	Instrumental Variable Approach	Chapter 4 Cameron
8)	Selection Bias in Cross-Sectional Data: Propensity Score Matching Problems	Chapter 25, Cameron
9)	Regression Discontinuity Design	Chapter 25, Cameron
10)	Difference-in-Difference Methods	Chapter 25, Cameron
11)	Introduction to Panel Data Analysis: Linear Panel Data Methods	Chapter 21, Cameron
12)	Fixed and Random Effects Models	Chapter 21, Cameron
13)	Generelized Method of Moments (GMM) Estimation in Panel data sets	Chapter 22, Cameron
14)	Generelized Method of Moments (GMM) Estimation in Panel data sets	Lecture Notes

Sources

Course Notes / Textbooks:	A. Colin Cameron ve Pravin K. Trivedi, Microeconometrics, New York: Cambridge University Press, 2005.
References:	A. Colin Cameron ve Pravin K. Trivedi, Microeconometrics Using Stata, Stata Press, 2010.

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Midterms	1	% 30
Final	1	% 30
Paper Submission	1	% 40
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 70
PERCENTAGE OF FINAL WORK		% 30
Total		% 100

ECTS / Workload Table

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	14	3	42
Application	4	1	4
Study Hours Out of Class	14	7	98
Midterms	1	2	2
Final	1	2	2
Total Workload			148

Contribution of Learning Outcomes to Programme Outcomes

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	As a world citizen, she is aware of global economic, political, social and ecological developments and trends.	1
2)	He/she is equipped to closely follow the technological progress required by global and local dynamics and to continue learning.	1
3)	Absorbs basic economic principles and analysis methods and uses them to evaluate daily events.	5
4)	Uses quantitative and statistical tools to identify economic problems, analyze them, and share their findings with relevant stakeholders.	5
5)	Understands the decision-making stages of economic units under existing constraints and incentives, examines the interactions and possible future effects of these decisions.	1
6)	Comprehends new ways of doing business using digital technologies. and new market structures.	1
7)	Takes critical approach to economic and social problems and develops analytical solutions.	5
8)	Has the necessary mathematical equipment to produce analytical solutions and use quantitative research methods.	5
9)	In the works he/she contributes, observes individual and social welfare together and with an ethical perspective.	1
10)	Deals with economic problems with an interdisciplinary approach and seeks solutions by making use of different disciplines.	2
11)	Generates original and innovative ideas in the works she/he contributes as part of a team.	1