ECUE Spatial econometrics and innovation

Code de l'ECUE : IMEESP3

Ce cours appartient à UE Innovations et dynamique industrielle (6 ECTS) qui contient 5 ECUE

Structure

EUR ELMI

Domaine disciplinaire

Sciences de gestion et du management

Lieu d'enseignement

Campus Saint Jean d'Angély

Niveau du cours

Master 2

Semestre

Semestre impair

Langue

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.

Responsable(s) du cours

Présentiel

14h de cours magistral
6h de travaux dirigés

PREREQUIS

Avant le début du cours, je dois ...

A preliminary knowledge in econometrics, linear algebra and calculus is required.
Two software will be used in this course: - Stata/R (preliminary knowledge required). - GeoDa (no preliminary knowledge required).

OBJECTIFS

A la fin de ce cours, je devrais être capable de...

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.

CONTENU

SAR, SEM, Durbin and SAC models

Aucune description
GWR regression

Aucune description
MLE, QMLE, GMM estimators for spatial models

Aucune description
Spatial weight matrixes

Aucune description
LISA and Moran’s statistics

Aucune description

Accéder au Syllabus complet (Authentification requise)

Important

Ce syllabus n’a aucune valeur contractuelle. Son contenu est susceptible d’évoluer en cours d’année : soyez attentifs aux dernières modifications.