Spatial Econometrics

2025/2026

Учебный год

ENG

Обучение ведется на английском языке

3

Кредиты

Кто читает:

Департамент экономики

Статус:

Курс по выбору

Когда читается:

1-й курс, 4 модуль

Преподаватель

Скоробогатов Александр Сергеевич

Course Syllabus

Abstract

The goal of this course is to give students methods and models of spatial econometrics, which are indispensable when dealing with spatial relationships between regions, firms, individuals, etc. The course includes techniques and methods to model spatial data taking into account interaction (spatial spillover) effects and spatial heterogeneity. Spatial econometrics is an active and fast-growing area of research, spurred by the increasing availability of spatial data, e.g., geo-referenced data or, more broadly, data with clustered observational units. These techniques are applicable in various fields of economics such as economic geography, urban economics, network economics, and applied studies.

Learning Objectives

• • Understand the conceptual foundations of spatial dependence and heterogeneity: students will learn to distinguish between spatial autocorrelation and spatial heterogeneity, and understand why standard econometric methods, e.g., OLS estimator, do not work in their presence. • Master key spatial econometric models and their interpretation: students will be able to specify, estimate, and interpret commonly used models such as the Spatial Lag Model (SAR), Spatial Error Model (SEM), and Spatial Durbin Model (SDM), including direct, indirect, and total effects. • Develop practical skills for spatial data analysis: students will gain hands-on experience in constructing spatial weights matrices, testing for spatial dependence (e.g., Moran’s I, LM tests), and implementing spatial regression. • Apply spatial econometrics to real-world problems: students will evaluate empirical studies and conduct their own spatial regression analysis using simulated and real data, while recognizing potential pitfalls such as misspecification and misinterpretation of spatial effects.

Expected Learning Outcomes

• • Diagnose and justify the need for spatial methods: test for spatial dependence (using Moran's I, LM tests) and spatial heterogeneity in cross-sectional and panel data; explain why OLS estimates are biased or inefficient in the presence of spatial effects; choose between spatial lag, spatial error, or spatial Durbin specifications based on diagnostic results and theoretical reasoning
• • Build and estimate core spatial econometric models: construct and standardize spatial weights matrices (contiguity, distance, k-nearest neighbors) and justify the choice; estimate SAR, SEM, and SDM models using maximum likelihood (ML) or instrumental variables (GS2SLS); compute and interpret direct, indirect (spillover), and total marginal effects correctly
• • Design and execute an applied spatial econometric study: formulate a research question that inherently involves spatial interactions (e.g., regional growth, housing prices, pollution diffusion); assemble and spatially align real-world geographic and socioeconomic data

Course Contents

• 1. Introduction to Spatial Econometrics
• 2. Spatial Data and their formalization
• 3. Spatial regressions
• 4. Spatial regression estimation
• 5. Spatial analysis using simulated/real data
• 6. Spatial postestimation analysis
• 7. Panel data spatial models

Assessment Elements

• Lecture and seminar attendance
• In-class participation
• Homework
• Project
  Project is a research work to be done by a group of 1-3 persons. They will have to take a data, real or simulated, and conduct research using the toolkit from the course.

Interim Assessment

• 2025/2026 4th module
  0.25 * Homework + 0.5 * Project + 0.05 * Lecture and seminar attendance + 0.2 * In-class participation

Bibliography

Recommended Core Bibliography

• Advances in Spatial Econometrics: methodology, tools and applications, , 2010

Recommended Additional Bibliography

• Econometric analysis of panel data, Baltagi, B. H., 2013

Authors

• BRODSKAYA NATALYA NIKOLAEVNA
• Skorobogatov Aleksandr Sergeevich