Information
Unit | INSTITUTE OF SOCIAL SCIENCES |
ECONOMETRICS (MASTER) (WITH THESIS) | |
Code | IEM768 |
Name | Applied Econometric Models |
Term | 2024-2025 Academic Year |
Term | Fall and Spring |
Duration (T+A) | 3-0 (T-A) (17 Week) |
ECTS | 6 ECTS |
National Credit | 3 National Credit |
Teaching Language | Türkçe |
Level | Yüksek Lisans Dersi |
Type | Normal |
Mode of study | Yüz Yüze Öğretim |
Catalog Information Coordinator | Doç. Dr. ÇİLER SİGEZE GÜNEY |
Course Instructor |
Doç. Dr. ÇİLER SİGEZE GÜNEY
(Bahar)
(A Group)
(Ins. in Charge)
|
Course Goal / Objective
The aim of this course is to introduce students to different econometric models, to make them analyse these models using statistical software programs such as STATA, R and to interpret the results obtained.
Course Content
Topics such as linear regression models, linear probability models, two-state models, multi-state models, ordinal regression models, quantile regression and linear instrumental variable regression models are covered.
Course Precondition
None
Resources
Üçdoğruk, Ş., & Şengül, S. (Eds.). (2021). Uygulamalarla Mikroekonometri.
Notes
Perez-Truglia, R. (2009). Applied econometrics using Stata. Manual Department of Economics, Harvard University.
Course Learning Outcomes
Order | Course Learning Outcomes |
---|---|
LO01 | Recognise different econometric models. |
LO02 | Recognise limited dependent variable models. |
LO03 | Applies limited dependent variable models in STATA and R programmes. |
LO04 | Recognise linear instrumental variable models. |
LO05 | Applies linear instrumental variable models in STATA and R programmes. |
Relation with Program Learning Outcome
Order | Type | Program Learning Outcomes | Level |
---|---|---|---|
PLO01 | Bilgi - Kuramsal, Olgusal | Explains contemporary concepts about Econometrics, Statistics, and Operation Research | 3 |
PLO02 | Bilgi - Kuramsal, Olgusal | Explains relationships between acquired knowledge about Econometrics, Statistics, and Operation Research | 3 |
PLO03 | Bilgi - Kuramsal, Olgusal | Explains how to apply acquired knowledge in the field to Economics, Business, and other social sciences | 2 |
PLO04 | Beceriler - Bilişsel, Uygulamalı | Performs conceptual analysis to develop solutions to problems | |
PLO05 | Beceriler - Bilişsel, Uygulamalı | Models problems with Mathematics, Statistics, and Econometrics | 3 |
PLO06 | Beceriler - Bilişsel, Uygulamalı | Interprets the results obtained from the most appropriate method to predict the model | 3 |
PLO07 | Beceriler - Bilişsel, Uygulamalı | Synthesizes the information obtained by using different sources within the framework of academic rules in a field of research | 3 |
PLO08 | Beceriler - Bilişsel, Uygulamalı | Uses acquired knowledge in the field to determine the vision, aim, and goals for an organization/institution | 2 |
PLO09 | Beceriler - Bilişsel, Uygulamalı | Searches for new approaches and methods to solve problems being faced | |
PLO10 | Beceriler - Bilişsel, Uygulamalı | Presents analysis results conveniently | 4 |
PLO11 | Beceriler - Bilişsel, Uygulamalı | Collects/analyzes data in a purposeful way | 4 |
PLO12 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Converts its findings into a master's thesis or a professional report in Turkish or a foreign language | 3 |
PLO13 | Beceriler - Bilişsel, Uygulamalı | Develops solutions for organizations using Econometrics, Statistics, and Operation Research | 4 |
PLO14 | Beceriler - Bilişsel, Uygulamalı | Uses a package program/writes a new code for Econometrics, Statistics, and Operation Research | 4 |
PLO15 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Performs an individual work to solve a problem with Econometrics, Statistics, and Operation Research | 3 |
PLO16 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Leads by taking responsibility individually and/or within the team | |
PLO17 | Yetkinlikler - Öğrenme Yetkinliği | Being aware of the necessity of lifelong learning, it constantly renews itself by following the current developments in the field of study | |
PLO18 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Interprets the feelings, thoughts and behaviors of the related persons correctly/expresses himself/herself correctly in written and verbal form | |
PLO19 | Yetkinlikler - Alana Özgü Yetkinlik | Interprets data on economic and social events by following current issues | |
PLO20 | Yetkinlikler - Alana Özgü Yetkinlik | Applies social, scientific and professional ethical values | 4 |
Week Plan
Week | Topic | Preparation | Methods |
---|---|---|---|
1 | An introduction to STATA and R sofwares | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Soru-Cevap, Anlatım, Alıştırma ve Uygulama, Gösterip Yaptırma |
2 | Linear regression model and Least Squares estimator, STATA and R software applications | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Soru-Cevap, Alıştırma ve Uygulama, Anlatım, Gösterip Yaptırma |
3 | Linear Probability Model and STATA and R software applications | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama, Gösterip Yaptırma |
4 | Probit Model and Logit Model | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama |
5 | STATA and R applications of Probit and Logit models | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama, Gösterip Yaptırma |
6 | Ordered Probit Model, Ordered Logit Model | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama |
7 | STATA ve R applications of Ordered Probit Model and Ordered Logit Model | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Soru-Cevap, Anlatım, Alıştırma ve Uygulama, Gösterip Yaptırma |
8 | Mid-Term Exam | Preparing for the midterm exam | Ölçme Yöntemleri: Yazılı Sınav, Ödev |
9 | Multinomial Probit Model and Multinomial Logit Model | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama |
10 | STATA ve R applications of Multinomial Probit Model and Multinomial Logit Model | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Gösterip Yaptırma, Alıştırma ve Uygulama |
11 | Quantile Regression | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama |
12 | STATA and R applications of Quantil Regression | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama, Gösterip Yaptırma |
13 | Linear instrumental variable models -IV, STATA and R applications | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama, Gösterip Yaptırma |
14 | Linear instrumental variable models -2SLS, STATA and R applications | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama, Gösterip Yaptırma |
15 | Linear instrumental variable models -GMM, STATA and R applications | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Soru-Cevap, Anlatım, Alıştırma ve Uygulama, Gösterip Yaptırma |
16 | Term Exams | Final exam preparation | Ölçme Yöntemleri: Yazılı Sınav, Ödev |
17 | Term Exams | Final exam preparation | Ölçme Yöntemleri: Yazılı Sınav, Ödev |
Student Workload - ECTS
Works | Number | Time (Hour) | Workload (Hour) |
---|---|---|---|
Course Related Works | |||
Class Time (Exam weeks are excluded) | 14 | 3 | 42 |
Out of Class Study (Preliminary Work, Practice) | 14 | 3 | 42 |
Assesment Related Works | |||
Homeworks, Projects, Others | 2 | 20 | 40 |
Mid-term Exams (Written, Oral, etc.) | 1 | 15 | 15 |
Final Exam | 1 | 15 | 15 |
Total Workload (Hour) | 154 | ||
Total Workload / 25 (h) | 6,16 | ||
ECTS | 6 ECTS |