Course Overview

This course provides students with the theoretical basics of Spatial
Econometrics enabling them to apply such theory empirically.

Learning Achievement

Competence

Course prerequisites

- Previous studies (Bachelor level) within the domain of economics.
- Knowledge of econometrics (STATA) is an advantage.

Grading Philosophy

- Continuous assessment: regular homework and/or presentations in
class.
-  A final essay is also required.

Course schedule

- Firstly, the main problems encountered when working with
geo-referenced data are reviewed and the need to apply spatial
econometrics is underlined.
- Secondly, the tools provided by the exploratory data analysis
(ESDA) are studied in order to detect the presence of spatial
autocorrelation in a given dataset and the concept of spatial weights
matrix is introduced (this part includes plotting variables in a map,
as well as statistical tests of global and local spatial
autocorrelation).
- Thirdly, an introduction is made to confirmatory spatial
econometrics with the regression of spatial models, including the
spatial lag model, the spatial error model, and the spatial Durbin
model.
- Fourthly, the consequences of ignoring spatial autocorrelation for
the estimation of regression models are addressed.
- Fifthly, the concept of direct and indirect effect recently
introduced by LeSage and Pace (2009) are presented.
- Finally, the most recent advances in Spatial Econometrics
(e.g.,spatial panel models) are covered.

Course type

Lectures and exercises in the computer lab.

Online Course Requirement

Instructor

Other information

Duration: One semester

Language of instruction: English
Mode of delivery: Face-to-face teaching; Practical exercises with dedicated software

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