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Syllabus ( CRP 580 )


   Basic information
Course title: Introduction to Spatial Econometrics
Course code: CRP 580
Lecturer: Assist. Prof. Merve YILMAZ
ECTS credits: 7.5
GTU credits: 3 (3+0+0)
Year, Semester: 1-2, Fall and Spring
Level of course: Second Cycle (Master's)
Type of course: Area Elective
Language of instruction: English
Mode of delivery: Face to face
Pre- and co-requisites: Introduction to statistics or equivalent
Professional practice: No
Purpose of the course: In this course, spatial dependence, estimations and analysis of spatial dependent data will be thought. The tendency to decide depending on the behavior of other spatial actors in which a spatial actor is involved is being discussed in recent years in many areas of social sciences. Control of spatial interdependencies reveals many rich relations in the field, and such control is necessary to obtain robust parameters in many studies. Matrix algebra, probability theory and maximum likelihood methods will be covered in the beginning of the course, later commonly used methods in spatial econometrics and their applications using R program will be explained.
   Learning outcomes Up

Upon successful completion of this course, students will be able to:

  1. Comprehend topics of spatial dependence, estimations and analysis of spatial dependent data.

    Contribution to Program Outcomes

    1. Define and manipulate advanced concepts of Urban and Regional Planning
    2. Formulate and solve complex urban planning problems,
    3. Review the literature critically pertaining to his/her research projects, and connect the earlier literature to his/her own results
    4. Acquire scientific knowledge and to work independently
    5. Work effectively in multi-disciplinary research teams
    6. Find out new methods to improve his/her knowledge.
    7. Defend research outcomes at seminars and conferences

    Method of assessment

    1. Written exam
    2. Homework assignment
  2. Conduct spatial econometric models using R statistical program.

    Contribution to Program Outcomes

    1. Define and manipulate advanced concepts of Urban and Regional Planning
    2. Formulate and solve complex urban planning problems,
    3. Prepare and manage independent research projects.
    4. Acquire scientific knowledge and to work independently
    5. Work effectively in multi-disciplinary research teams
    6. Develop an awareness of continuous learning in relation with modern technology

    Method of assessment

    1. Homework assignment
  3. Associate several economic topics with space.

    Contribution to Program Outcomes

    1. Define and manipulate advanced concepts of Urban and Regional Planning
    2. Formulate and solve complex urban planning problems,
    3. Evaluate and criticize recent planning and design policies, and administrative and legislative structures in Turkey
    4. Acquire scientific knowledge and to work independently
    5. Work effectively in multi-disciplinary research teams
    6. Find out new methods to improve his/her knowledge.

    Method of assessment

    1. Written exam
    2. Homework assignment
   Contents Up
Week 1: Probability theory (a short review)
Week 2: Matrix algebra (a short review)
Week 3: Spatial dependence
Week 4: Motivating spatial econometric models (Homework)
Week 5: Spatial data
Week 6: Methods of spatial data analysis
Week 7: Maximum likelihood estimation
Week 8: Parameter estimation (Homework)
Week 9: Midterm exam
Week 10: Spatial autoregressive model
Week 11: Interpreting estimated parameters
Week 12: Model comparison methods (Homework)
Week 13: Discrete dependent variable spatial models
Week 14: Spatial and temporal econometric models
Week 15*: ---
Week 16*: Final Exam
Textbooks and materials: LeSage, J., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC press.
Recommended readings: 1. Anselin, L., Florax, R., & Rey, S. J. (Eds.). (2004). Advances in spatial econometrics: methodology, tools and applications. Springer.
2. Beck, N., K. Gleditsch, and K. Beardsley. (2006). “Space is More than Geography: Using Spatial Econometrics in the Study of Political Economy.” International Studies Quarterly 50: 27-44.
3. Elhorst, J. P. (2014). Spatial Econometrics. Springer Berlin Heidelberg.
4. Franzese, R.J. and J.C. Hays. (2007). “Spatial-Econometric Models of Cross-Sectional Interdependence in Political Science Panel and Time-Series-Cross-Section Data.” Political Analysis 15(2): 140-164.
5. Franzese, R.J, and J.C. Hays. (2008). “Empirical Models of Spatial Interdependence” In Oxford Handbook of Political Methodology, Eds. Janet Box-Steffensmeier, Henry Brady, and David Collier, pp. 570-604, Oxford U.K.: Oxford University Press.
6. Ward, M.D. and K.S. Gleditsch. (2008). Spatial Regression Models. Sage.
  * Between 15th and 16th weeks is there a free week for students to prepare for final exam.
Assessment Up
Method of assessment Week number Weight (%)
Mid-terms: 9 30
Other in-term studies: 0
Project: 0
Homework: 4, 8, 12 30
Quiz: 0
Final exam: 16 40
  Total weight:
(%)
   Workload Up
Activity Duration (Hours per week) Total number of weeks Total hours in term
Courses (Face-to-face teaching): 3 13
Own studies outside class: 5 14
Practice, Recitation: 0 0
Homework: 10 3
Term project: 0 0
Term project presentation: 0 0
Quiz: 0 0
Own study for mid-term exam: 16 1
Mid-term: 3 1
Personal studies for final exam: 21 1
Final exam: 3 1
    Total workload:
    Total ECTS credits:
*
  * ECTS credit is calculated by dividing total workload by 25.
(1 ECTS = 25 work hours)
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