Descrizione del progetto
Strumenti econometrici per analizzare l’eterogeneità dei modelli nei social network
Considerato l’ampio ventaglio di applicazioni di socializzazione in rete, l’analisi delle interazioni sociali è emersa come un ambito chiave di interesse per la ricerca. I modelli di interazione personali forniscono informazioni sul modo in cui le persone si condizionano tra loro. I modelli basati su agenti sono ampiamente utilizzati per l’analisi di tali social network. La rete è costruita dal basso verso l’alto, assegnando attributi alle entità autonome del processo decisionale, denominate agenti, che sono per natura eterogenee. Tenere conto di un’eterogeneità non osservata è un processo impegnativo, compromesso da problemi di identificazione, stima e calcolo. Il progetto NETWORK, finanziato dall’UE, svilupperà un nuovo approccio non parametrico che affronti questi tre problemi. I suoi strumenti econometrici descriveranno in che modo il cambiamento in un individuo condizioni gli attributi degli altri, tenendo conto dell’eterogeneità non osservata attraverso un approccio di effetti casuali.
Obiettivo
The overarching goal of this project is to develop a coherent set of econometric methods to deal with unobserved heterogeneity in the analysis of social interactions between agents. Such heterogeneity is well recognized to be important. It is often of great interest to document the degree of heterogeneity, evaluate its impact, and uncover the existence and form of any complementarities that may exist between agents. With the growing availability of network data, questions of this kind are increasingly being asked in applied work. The development of appropriate econometric tools to answer them has, however, not followed suit. If anything, recent theoretical work has pointed at substantial difficulties with the so called fixed-effect approach currently serving as the workhorse tool.
This project recognizes the potential of taking a random-effect view. For settings where agents interact in pairs, such a view has received some attention in the literature. However, to date, it struggles with issues of identification, estimation, and computation. We will develop a new nonparametric approach that provides a solution to each of these three issues. We will next venture forward and extend this framework to situations where agents interact in larger groups. Both collaborative and non-collaborative settings will be considered, thereby covering team production, competition, and peer effects. Special attention will be given to recovering treatment effects in the presence of social interactions, where interference on unobservable confounders is an issue. For situations where data limitations prevent a fully nonparametric approach, instrumental-variable methods that build on flexible functional form restrictions will be developed. The statistical properties of the proposed estimators will be derived, software implementation will be provided, and empirical illustrations will be presented to highlight the usefulness of the methods.
Campo scientifico
Parole chiave
Programma(i)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Argomento(i)
Meccanismo di finanziamento
HORIZON-ERC - HORIZON ERC GrantsIstituzione ospitante
31080 Toulouse
Francia