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| Unit | INSTITUTE OF SOCIAL SCIENCES |
| ECONOMETRICS (MASTER) (WITH THESIS) | |
| Code | IEM747 |
| Name | Spatial Econometrics I |
| Term | 2018-2019 Academic Year |
| Term | Fall |
| 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 Instructor |
The current term course schedule has not been prepared yet.
|
Course Goal / Objective
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
Resources
Notes
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 of 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 | Beceriler - Bilişsel, Uygulamalı | Uses a package program/writes a new code for Econometrics, Statistics, and Operation Research | 5 |
| PLO15 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Performs an individual work to solve a problem with Econometrics, Statistics, and Operation Research | 4 |
| 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 | 3 |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 5 | Some Important Spatial Definitions: Exercises and R coding. | Students will be prepared by studying relevant subjects from source books according to the weekly program | |
| 6 | Spatial Linear Regression Models: Generalities | Students will be prepared by studying relevant subjects from source books according to the weekly program | |
| 7 | Spatial Linear Regression Models:Pure spatial autoregression | Students will be prepared by studying relevant subjects from source books according to the weekly program | |
| 8 | Mid-Term Exam | ||
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 15 | Spatial Linear Regression Models:Exercises and R coding. | Students will be prepared by studying relevant subjects from source books according to the weekly program | |
| 16 | Term Exams | ||
| 17 | Term Exams |
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 | ||