EKMZ401 Econometrics III

3 ECTS - 3-0 Duration (T+A)- 7. Semester- 3 National Credit

Information

Code EKMZ401
Name Econometrics III
Term 2024-2025 Academic Year
Semester 7. Semester
Duration (T+A) 3-0 (T-A) (17 Week)
ECTS 3 ECTS
National Credit 3 National Credit
Teaching Language Türkçe
Level Lisans Dersi
Type Normal
Mode of study Yüz Yüze Öğretim
Catalog Information Coordinator Prof. Dr. SEDA ŞENGÜL
Course Instructor Prof. Dr. SEDA ŞENGÜL (A Group) (Ins. in Charge)
Prof. Dr. SEDA ŞENGÜL (B Group) (Ins. in Charge)


Course Goal / Objective

The aim of this course is to provide students with the basic concepts of econometrics and to strengthen econometric background. This course also aims to equip students with skills to carry out independent applied research and be able to apply certain econometric methods to economic problems.

Course Content

This course, which is the continuation of econometrics I and econometrics II, will focus on topics such as simultaneous equations estimation methods, dynamic models, discrete choice models, limited dependent variable models, panel data analysis, time series analysis.

Course Precondition

No prerequisites are required.

Resources

Damodar N. Gujarati "Introduction to Econometrics"

Notes

Lectures


Course Learning Outcomes

Order Course Learning Outcomes
LO01 The basic subjects of econometrics are well known
LO02 The student is able to estimate and interpret econometric models.
LO03 The student gaines skills to carry out independent applied research as well as to develop new econometric methods
LO04 The student can identify any econometric problems and to develop the solutions.
LO05 The student can produce solutions to any econometric problems in an econometric model.


Relation with Program Learning Outcome

Order Type Program Learning Outcomes Level
PLO01 Bilgi - Kuramsal, Olgusal Explain the basic concepts and theorems in the fields of Econometrics, Statistics and Operations research 3
PLO02 Bilgi - Kuramsal, Olgusal Acquires basic Mathematics, Statistics and Operation Research concepts 4
PLO03 Bilgi - Kuramsal, Olgusal Describes the necessary concepts of Business 4
PLO04 Beceriler - Bilişsel, Uygulamalı Equipped with the foundations of Economics, and develops Economic models 3
PLO05 Beceriler - Bilişsel, Uygulamalı Models problems with Mathematics, Statistics, and Econometrics 5
PLO06 Beceriler - Bilişsel, Uygulamalı Has the ability to analyze/interpret at the conceptual level to develop solutions to problems 4
PLO07 Beceriler - Bilişsel, Uygulamalı Collects/analyses data 4
PLO08 Beceriler - Bilişsel, Uygulamalı Interprets the results analyzed with the model 3
PLO09 Beceriler - Bilişsel, Uygulamalı Combines the information obtained from different sources within the framework of academic rules in a field which does not research 3
PLO10 Beceriler - Bilişsel, Uygulamalı It develops traditional approaches, practices and methods into new working methods when it deems necessary 4
PLO11 Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği Leads by taking responsibility individually and/or within the team 4
PLO12 Yetkinlikler - Öğrenme Yetkinliği In addition to herself/himself professional development, constantly improves in scientific, cultural, artistic and social fields in line with interests and abilities 5
PLO13 Yetkinlikler - Öğrenme Yetkinliği Being aware of the necessity of lifelong learning, it constantly renews itself by following the current developments in its field. 3
PLO14 Yetkinlikler - İletişim ve Sosyal Yetkinlik Uses a package program of Econometrics, Statistics, and Operation Research 4
PLO15 Yetkinlikler - İletişim ve Sosyal Yetkinlik Uses Turkish and at least one other foreign language, academically and in the business context 3
PLO16 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
PLO17 Yetkinlikler - Alana Özgü Yetkinlik Interprets data on current economic and social issues
PLO18 Yetkinlikler - Alana Özgü Yetkinlik Applies social, scientific and professional ethical values


Week Plan

Week Topic Preparation Methods
1 Model specification, model specifation error and types of model specification error Students will be prepared by studying relevant subjects from source books according to the weekly program. Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Alıştırma ve Uygulama
2 Tests of model specification error , errors of measurement and model selection criteria. Students will be prepared by studying relevant subjects from source books according to the weekly program. Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Alıştırma ve Uygulama
3 Dynamanic models: Estimation of Distributed -Lag models. The Koyck Approach, Adaptive expectation, Partial adjustment Students will be prepared by studying relevant subjects from source books according to the weekly program. Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Alıştırma ve Uygulama
4 Estimation of Autoregressive models: Almon approach, polinominal spline distrubuted lags, exogeneity tests, the wu-hausman test 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 Simultaneous equation models:specifation and identification in Simultaneous equation 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
6 Estimation and inference in simultaneous equation models: indirect least squares, generalized least squares, the two-stage least squares estimator, the limited information maximum likelihood, the full information maximum likelihood 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 Maximum likelihood (ML): properties of ML estimators. ML estimation of linear 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
8 Mid-Term Exam Students prepare for mid-term exam Ölçme Yöntemleri:
Yazılı Sınav
9 aximum likelihood (ML): likelihood ratio test, wald test, lagrange multiplier tests 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 Discrete choice models: Linear probability model, 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
11 Limited dependent variables: truncating, censoring, and sample selection 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 Tobit model, Heckman 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
13 Panel data analysis: general terms of panel data, advantages of panel data, pooled least squares Students will be prepared by studying relevant subjects from source books according to the weekly program. Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Alıştırma ve Uygulama
14 panel data analysis fixed effects models and random effects 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
15 Time series modeling: ARMA processes, testing and estimation for stationarity, forecast Students will be prepared by studying relevant subjects from source books according to the weekly program. Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Alıştırma ve Uygulama
16 Term Exams Students prepare for final exam Ölçme Yöntemleri:
Yazılı Sınav
17 Term Exams Students prepare for final exam Ölçme Yöntemleri:
Yazılı Sınav


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 2 28
Assesment Related Works
Homeworks, Projects, Others 0 0 0
Mid-term Exams (Written, Oral, etc.) 1 6 6
Final Exam 1 10 10
Total Workload (Hour) 86
Total Workload / 25 (h) 3,44
ECTS 3 ECTS

Update Time: 08.05.2024 11:38