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1 – 3 of 3Tiziano Arduini, Eleonora Patacchini and Edoardo Rainone
The authors generalize the standard linear-in-means model to allow for multiple types with between and within-type interactions. The authors provide a set of identification…
Abstract
The authors generalize the standard linear-in-means model to allow for multiple types with between and within-type interactions. The authors provide a set of identification conditions of peer effects and consider a two-stage least squares estimation approach. Large sample properties of the proposed estimators are derived. Their performance in finite samples is investigated using Monte Carlo simulations.
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This chapter is concerned with the estimation of spillover effects when outcomes arise as a consequence of bilateral interactions instead of from individual actions. In this type…
Abstract
This chapter is concerned with the estimation of spillover effects when outcomes arise as a consequence of bilateral interactions instead of from individual actions. In this type of environments, outcomes are generated on links instead of on nodes of a network, like bilateral prices in over-the-counter markets. The author proposes a link-based spatial autoregressive (SAR) model and discusses identification conditions and a two step least square estimation procedure. The author shows analytically that using a standard node-based SAR, which models nodes instead of links’ outcomes, produces misleading results when the data generating process is link-based. The methodology is illustrated using Monte Carlo experiments and real data from an interbank network.
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