TÜRKÇE
ENGLISH
Information
| Code | IEM747 |
| Name | Spatial Econometrics I |
| 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
The aim of this course is to introduce 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 | Estimates spatial linear regression models. |
| LO04 | Uses R programming language fluently. |
| LO05 | 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 | 5 |
| 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 | |
| PLO05 | Beceriler - Bilişsel, Uygulamalı | Models problems with Mathematics, Statistics, and Econometrics | 5 |
| PLO06 | Beceriler - Bilişsel, Uygulamalı | Interprets the results obtained from the most appropriate method to predict the model | 5 |
| 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 | |
| PLO10 | Beceriler - Bilişsel, Uygulamalı | Presents analysis results conveniently | 3 |
| 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 | 3 |
| PLO13 | Beceriler - Bilişsel, Uygulamalı | Develops solutions for organizations using Econometrics, Statistics, and Operation Research | 3 |
| PLO14 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Performs an individual work to solve a problem with Econometrics, Statistics, and Operation Research | 4 |
| 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 | 3 |
| 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 | The Classical Linear Regression Model:Non-sphericity of the disturbances; Endogeneity. | Students will be prepared by studying relevant subjects from source books according to the weekly program. | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 2 | The Classical Linear Regression Model: Exercises and R coding. | Students will be prepared by studying relevant subjects from source books according to the weekly program. | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama, Gösterip Yaptırma |
| 3 | Some Important Spatial Definitions:The Spatial Weight Matrix W and the definition of Spatial Lag. | Students will be prepared by studying relevant subjects from source books according to the weekly program. | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 4 | Some Important Spatial Definitions: Testing Spatial Autocorrelation among OLS Residuals without an Explicit Alternative Hypothesis. | Students will be prepared by studying relevant subjects from source books according to the weekly program. | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 5 | Some Important Spatial Definitions: Exercises, R Coding and GeoDa 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 |
| 6 | Spatial Linear Regression Models: Generalities. | Students will be prepared by studying relevant subjects from source books according to the weekly program. | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 7 | Spatial Linear Regression Models: Pure Spatial Autoregression. | 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 | Spatial Linear Regression Models: The Classical Model with Spatially Lagged Non-stochastic Regressors. | Students will be prepared by studying relevant subjects from source books according to the weekly program. | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 10 | Spatial Linear Regression Models: The Spatial Error Model (SEM). | Students will be prepared by studying relevant subjects from source books according to the weekly program. | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 11 | Spatial Linear Regression Models: The Spatial Lag Model (SLM) | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Soru-Cevap |
| 12 | Spatial Linear Regression Models: The General SARAR(1,1) Model. | Students will be prepared by studying relevant subjects from source books according to the weekly program. | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 13 | Spatial Linear Regression Models: Testing Spatial Autocorrelation among the Residuals with an Explicit Alternative Hypothesis. | Students will be prepared by studying relevant subjects from source books according to the weekly program. | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 14 | Spatial Linear Regression Models: Interpretation of the Parameters in Spatial Econometric Models. | Students will be prepared by studying relevant subjects from source books according to the weekly program. | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 15 | Spatial Linear Regression Models: Exercises, R Coding and GeoDa Applications. | Students will be prepared by studying relevant subjects from source books according to the weekly program. | Öğretim Yöntemleri: Anlatım, Gösterip Yaptırma, Problem Çözme |
| 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 | ||