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ECUE Spatial econometrics and innovation
ECUE's code : IMEESP3
EUR ELMI
Sciences de gestion et du management
Campus Saint Jean d'Angély
Master 2
Semestre impair
Anglais
PRESENTATION
This course introduces students to spatial econometrics, in order to provide them a useful technical tool to analyze innovation, industrial, and growth dynamics.
Spatial econometrics is a subfield of econometrics which intersects with spatial analysis, and itis used to analyze spatial dependence among observations, which often arises when observations are collected from points orregions located in space.
Spatial econometrics represents a methodology which has been acquiring more and more relevance during the recent years within the econometric literature, and it has been used extensively in different fields in economics, such as industrial organization, international economics, and growth.
The course is structured as follows.The first 15 hours will be devoted to theory, in order to provide to students the necessary theoretical foundations of spatial econometrics.
Subsequently, 5 hours will involve empirical exercises with the usage of Stata, R and GeoDa.This will help students to familiarize with the most common routines utilized in Stata and R for spatial econometric analysis.
Course's manager(s)
In class
- 14h of lectures
- 6h of directed studies
PREREQUISITES
Before the start of the course, I must ...
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A preliminary knowledge in econometrics, linear algebra and calculus is required.
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Two software will be used in this course: - Stata/R (preliminary knowledge required). - GeoDa (no preliminary knowledge required).
OBJECTIVES
By the end of this course, I should be able to...
- Get necessary knowledge and tools in order to be able to conduct spatial econometric analysis.
- Acquire most common spatial models which have been developed in the literature.
- Acquire the different approaches used to construct spatial weight matrices, and local and global indexes of spatial autocorrelation.
CONTENT
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SAR, SEM, Durbin and SAC models
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GWR regression
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MLE, QMLE, GMM estimators for spatial models
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Spatial weight matrixes
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LISA and Moran’s statistics
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Access to complete Syllabus (Authentification required)
Important
This syllabus has no contractual value. Its content is subject to change throughout this year: be aware to the last updates