IEM748 Spatial Econometrics II

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

Information

Code IEM748
Name Spatial Econometrics II
Term 2024-2025 Academic Year
Semester . Semester
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 Dr. Öğr. Üyesi FELA ÖZBEY


Course Goal / Objective

The aim of this course is to introduce advanced methods modelling spatial autocorrelations in regrresion analysis, and R programming language.

Course Content

The Classical Linear Regression Model, Some Important Spatial Definitions, Spatial Linear Regression Models, applications with R programming language.

Course Precondition

None

Resources

Giuseppe Arbia (2006) Spatial Econometrics_ Statistical Foundations and Applications to Regional Convergence (Advances in Spatial Science), Springer, ISBN-13 978-3-540-32304-4

Notes

Roger S. Bivand , Edzer J. Pebesma Virgilio Gómez-Rubio (2008), Applied Spatial Data Analysis with R, Springer, ISBN 978-0-387-78170-9


Course Learning Outcomes

Order Course Learning Outcomes
LO01 Specifies spatial relations.
LO02 Chooses the most appropriate model for spatial correlations.
LO03 Applies advanced Spatial Econometrics methods.
LO04 Estimates spatial linear regression models.
LO05 Uses R programming language fluently.
LO06 Codes techniques and models taught in this course.


Relation with Program Learning Outcome

Order Type Program Learning Outcomes Level
PLO01 Bilgi - Kuramsal, Olgusal Explains contemporary concepts about Econometrics, Statistics, and Operation Research 5
PLO02 Bilgi - Kuramsal, Olgusal Explains relationships between acquired knowledge about Econometrics, Statistics, and Operation Research 4
PLO03 Bilgi - Kuramsal, Olgusal Explains how to apply acquired knowledge in the field to Economics, Business, and other social sciences 4
PLO04 Beceriler - Bilişsel, Uygulamalı Performs conceptual analysis to develop solutions to problems 3
PLO05 Beceriler - Bilişsel, Uygulamalı Models problems with Mathematics, Statistics, and Econometrics 4
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 that does not research 3
PLO08 Beceriler - Bilişsel, Uygulamalı Uses acquired knowledge in the field to determine the vision, aim, and goals for an organization/institution
PLO09 Beceriler - Bilişsel, Uygulamalı Searches for new approaches and methods to solve problems being faced 3
PLO10 Beceriler - Bilişsel, Uygulamalı Presents analysis results conveniently 4
PLO11 Beceriler - Bilişsel, Uygulamalı Collects/analyzes data in a purposeful way 5
PLO12 Yetkinlikler - İletişim ve Sosyal Yetkinlik Converts its findings into a master's thesis or a professional report in Turkish or a foreign language
PLO13 Beceriler - Bilişsel, Uygulamalı Develops solutions for organizations using Econometrics, Statistics, and Operation Research
PLO14 Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği Performs an individual work to solve a problem with Econometrics, Statistics, and Operation Research 5
PLO15 Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği Leads by taking responsibility individually and/or within the team
PLO16 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 2
PLO17 Yetkinlikler - İletişim ve Sosyal Yetkinlik Uses a package program of Econometrics, Statistics, and Operation Research or writes a new code 5
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 3
PLO20 Yetkinlikler - Alana Özgü Yetkinlik Applies social, scientific and professional ethical values


Week Plan

Week Topic Preparation Methods
1 Introduction: Some Important Spatial Definitions. Students will be prepared by studying relevant subjects from source books according to the weekly program Öğretim Yöntemleri:
Anlatım, Soru-Cevap
2 Introduction: Spatial Linear Regression Models Students will be prepared by studying relevant subjects from source books according to the weekly program Öğretim Yöntemleri:
Anlatım, Soru-Cevap
3 Advanced Topics in Spatial Econometrics: Heteroscedastic innovations. Students will be prepared by studying relevant subjects from source books according to the weekly program Öğretim Yöntemleri:
Anlatım, Soru-Cevap
4 Advanced Topics in Spatial Econometrics: Spatial models for binary response variables Students will be prepared by studying relevant subjects from source books according to the weekly program Öğretim Yöntemleri:
Anlatım, Soru-Cevap
5 Advanced Topics in Spatial Econometrics :Spatial panel data models Students will be prepared by studying relevant subjects from source books according to the weekly program Öğretim Yöntemleri:
Anlatım, Soru-Cevap
6 Advanced Topics in Spatial Econometrics: Spatial panel data models (continue) Students will be prepared by studying relevant subjects from source books according to the weekly program Öğretim Yöntemleri:
Anlatım, Soru-Cevap
7 Advanced Topics in Spatial Econometrics: Non-stationary spatial econometric models. Students will be prepared by studying relevant subjects from source books according to the weekly program Öğretim Yöntemleri:
Anlatım, Soru-Cevap
8 Mid-Term Exam Ölçme Yöntemleri:
Ödev
9 Advanced Topics in Spatial Econometrics: Exercises and R applications. Students will be prepared by studying relevant subjects from source books according to the weekly program Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama, Gösterip Yaptırma
10 Alternative Model Specifications for Big Datasets: Introduction. Students will be prepared by studying relevant subjects from source books according to the weekly program Öğretim Yöntemleri:
Anlatım, Soru-Cevap
11 Alternative Model Specifications for Big Datasets: The MESS specification. Students will be prepared by studying relevant subjects from source books according to the weekly program Öğretim Yöntemleri:
Anlatım, Soru-Cevap
12 Alternative Model Specifications for Big Datasets: The unilateral approximation approach. Students will be prepared by studying relevant subjects from source books according to the weekly program Öğretim Yöntemleri:
Anlatım, Soru-Cevap
13 Alternative Model Specifications for Big Datasets: A composite likelihood approach. Students will be prepared by studying relevant subjects from source books according to the weekly program Öğretim Yöntemleri:
Anlatım, Soru-Cevap
14 Alternative Model Specifications for Big Datasets: Exercises and R applications. Students will be prepared by studying relevant subjects from source books according to the weekly program Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama, Gösterip Yaptırma
15 Other Topics: Robust estimation methods, Spatial Seemingly Unrelated Regression. Students will be prepared by studying relevant subjects from source books according to the weekly program Öğretim Yöntemleri:
Anlatım, Soru-Cevap
16 Term Exams Ölçme Yöntemleri:
Ödev
17 Term Exams Ölçme Yöntemleri:
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 5 70
Assesment Related Works
Homeworks, Projects, Others 0 0 0
Mid-term Exams (Written, Oral, etc.) 1 15 15
Final Exam 1 30 30
Total Workload (Hour) 157
Total Workload / 25 (h) 6,28
ECTS 6 ECTS

Update Time: 09.05.2024 04:21