Institutional Affiliation: Massachusetts Institute of Technology
|Contingent Linear Financial Networks|
with , : w26814
In this paper, we develop a methodology to estimate hidden linear networks when only an aggregate outcome is observed. The aggregate observable variable is a linear combination of the different networks and it is assumed that each network corresponds to the transmission mechanism of different shocks. We implement the methodology to estimate financial networks among US financial institutions. Credit Default Swap rates are the observable variable and we show that more than one network is needed to understand the dynamic behavior exhibited in the data.
|Bayesian Learning in Social Networks|
with , , : w14040
We study the perfect Bayesian equilibrium of a model of learning over a general social network. Each individual receives a signal about the underlying state of the world, observes the past actions of a stochastically-generated neighborhood of individuals, and chooses one of two possible actions. The stochastic process generating the neighborhoods defines the network topology (social network). The special case where each individual observes all past actions has been widely studied in the literature. We characterize pure-strategy equilibria for arbitrary stochastic and deterministic social networks and characterize the conditions under which there will be asymptotic learning -- that is, the conditions under which, as the social network becomes large, individuals converge (in probability) to ...
Published: Daron Acemoglu & Munther A. Dahleh & Ilan Lobel & Asuman Ozdaglar, 2011. "Bayesian Learning in Social Networks," Review of Economic Studies, Oxford University Press, vol. 78(4), pages 1201-1236. citation courtesy of