Methoden vergelijken

	Netwerkeconometrie (Peer Effects)	Centraaliteitsanalyse	Spatiaal Lag Model (SAR / Spatiale Autoregressie)
Vakgebied	Econometrie	Netwerkanalyse	Ruimtelijke analyse
Familie	Regression model	Process / pipeline	Regression model
Jaar van ontstaan	2009	1979	1988
Grondlegger	Yann Bramoullé, Habiba Djebbari & Bernard Fortin	Linton C. Freeman	Anselin (textbook formalisation); LeSage & Pace
Type	Linear-in-means peer effects regression	Descriptive / exploratory network measure family	Spatial autoregressive regression
Oorspronkelijke bron	Bramoullé, Y., Djebbari, H., & Fortin, B. (2009). Identification of peer effects through social networks. Journal of Econometrics, 150(1), 41–55.	Freeman, L.C. (1979). Centrality in Social Networks: Conceptual Clarification. Social Networks, 1(3), 215-239.	Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic.
Aliassen	Social Interactions Model, Peer Effects Model, Social Network Regression, Ağ Ekonometrisi	Merkeziyet Analizi (Degree, Betweenness, Eigenvector), node centrality, centrality measures, graph centrality	SAR model, spatial autoregressive model, spatial lag, Uzamsal Gecikme Modeli (SAR / Spatial Lag)
Verwant	3	5	5
Samenvatting	Network econometrics estimates how individuals' outcomes are causally shaped by the behaviour and characteristics of their social-network neighbours. Formalised by Bramoullé, Djebbari, and Fortin (2009), the framework embeds a row-normalised adjacency matrix into a linear regression, separating endogenous peer effects (imitation of outcomes), exogenous contextual effects (influence of neighbours' attributes), and correlated effects (shared environment), while using network topology to construct valid instruments.	Centrality analysis is a family of network-analytic measures, formalized by Freeman (1979), that quantifies the structural importance of individual nodes within a graph. Each centrality index captures a distinct mechanism of influence: degree centrality reflects direct connectivity, betweenness centrality identifies nodes that broker information flow, closeness centrality captures proximity to all others, and eigenvector centrality (along with PageRank) rewards connection to highly connected neighbors.	The Spatial Lag Model is an autoregressive regression that assumes spatial dependence in the dependent variable itself: the outcome values of neighbouring units enter the model as an explanatory term (ρWy). It was formalised in Anselin's Spatial Econometrics (1988) and developed further by LeSage and Pace (2009), and it decomposes spillover effects into direct, indirect, and total impacts.