2021/2022

Геопространственный анализ в экономике

Лучший по критерию «Новизна полученных знаний»

Статус: Дисциплина общефакультетского пула

Кто читает: Департамент экономики

Где читается: Санкт-Петербургская школа экономики и менеджмента

Когда читается: 1 модуль

Охват аудитории: для всех кампусов НИУ ВШЭ

Преподаватели: Иванова Вера Ивановна

Язык: английский

Кредиты: 3

Контактные часы: 30

Course Syllabus

Abstract

Over past decades, economic models have been gradually incorporating the existence of spatial relationships between regions, firms, individuals, etc. Geospatial Analysis in Economics course includes techniques and methods to model spatial data considering interaction (spatial spillover) effects and spatial heterogeneity. This course aims at getting acquaintance with main techniques of spatial statistics and spatial econometrics along with the main issues posed by the geospatial data analysis and by the construction and estimation of spatial econometric models. Geospatial tools are an active and fast-growing methodology of research, spurred by the increasing availability of spatial data, i. e. geo-referenced data. These techniques, many of which are still in their early development, use different analytic approaches and are applied in different fields of economics.

Learning Objectives

• This course aims at getting acquaintance with main techniques of geospatial data analysis in economics and with basic spatial econometric models.

Expected Learning Outcomes

• Be able to analyze spatial point datasets
• Be able to estimate basic spatial econometric regressions using modern econometrics software.
• Be able to import and export spatial data
• Be able to motivate and interpret basic spatial econometric regressions.
• Be able to test for spatial dependence in the model.
• Know how to analyze and model areal data
• Know how to visualize spatial data
• Know main spatial regression models
• Know main types of spatial data

Course Contents

• Introduction to geospatial data analysis in economics.
• Visualizing spatial data.
• Vector Data Analysis
• Spatial Point Pattern Analysis.
• Modelling Areal Data.
• Basic spatial regression models.
• Estimating spatial regression models
• Motivating and interpreting spatial econometric models.

Assessment Elements

• In-class tests
• Homework
• final exam in the form of the oral defense of a written essay

Interim Assessment

• 2021/2022 1st module
  0.5 * final exam in the form of the oral defense of a written essay + 0.25 * Homework + 0.25 * In-class tests

Bibliography

Recommended Core Bibliography

• Applied spatial data analysis with R, Bivand, R. S., 2008
• Applied spatial data analysis with R, Bivand, R., 2008
• Bivand, R., Pebesma, E. J., & Gómez-Rubio, V. (2013). Applied Spatial Data Analysis with R (Vol. 2nd ed). New York, NY: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=601853
• Introduction to spatial econometrics, LeSage, J., 2009
• Introduction to spatial econometrics, LeSage, J.P., 2009

Recommended Additional Bibliography

• Arbia G. A Primer for Spatial Econometrics: With Applications in R. Basingstoke: Palgrave Macmillan, 2014.
• Arbia, G. (2014). A Primer for Spatial Econometrics : With Applications in R. Basingstoke: Palgrave Macmillan. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=998543
• L. Anselin. (2013). Spatial Econometrics: Methods and Models (Vol. 1988). Springer.