nep-net New Economics Papers
on Network Economics
Issue of 2017‒12‒03
five papers chosen by
Pedro CL Souza
Pontifícia Universidade Católica do Rio de Janeiro

  1. Coordination on Networks By Leister, Matthew; Zenou, Yves; Zhou, Junjie
  2. Not too close, not too far: testing the Goldilocks principle of ‘optimal’ distance in innovation networks By Fitjar, Rune Dahl; Hubert, Franz; Rodríguez-Pose, Andrés
  3. A Bayesian methodology for systemic risk assessment in financial networks By Gandy, Axel; Veraart, Luitgard A. M.
  4. Nonparametric Identification in Index Models of Link Formation By Wayne Yuan Gao
  5. Network models of financial systemic risk: A review By Fabio Caccioli; Paolo Barucca; Teruyoshi Kobayashi

  1. By: Leister, Matthew; Zenou, Yves; Zhou, Junjie
    Abstract: We study a coordination game among agents on a network, choosing whether or not to take an action that yields value increasing in the actions of neighbors. In a standard global game setting, players receive noisy information of the technology's common state-dependent value. We show the existence and uniqueness of a pure equilibrium in the noiseless limit. This equilibrium partitions players into coordination sets, within members take a common cutoff strategy and are path connected. We derive an algorithm for calculating limiting cutoffs, and provide necessary and sufficient conditions for agents to inhabit the same coordination set. The strategic effects of perturbations to players' underlining values are shown to spread throughout but be contained within the perturbed players' coordination sets.
    Keywords: coordination; global games; network partition; welfare.
    JEL: C72 D85 Z13
    Date: 2017–10
  2. By: Fitjar, Rune Dahl; Hubert, Franz; Rodríguez-Pose, Andrés
    Abstract: This paper analyses how the formation of collaboration networks affects firm-level innovation by applying the ‘Goldilocks principle’. The ‘Goldilocks principle’ of optimal distance in innovation networks postulates that the best firm-level innovation results are achieved when the partners involved in the network are located at the ‘right’ distance, i.e. ‘not too close and not too far’ from one another, across non-geographical proximity dimensions. This principle is tested on a survey of 542 Norwegian firms conducted in 2013, containing information about firm-level innovation activities and key innovation partners. The results of the ordinal logit regression analysis substantiate the Goldilocks principle, as the most innovative firms are found among those that collaborate with partners at medium levels of proximity for all non-geographical dimensions. The analysis also underscores the importance of the presence of a substitution–innovation mechanism, with geographical distance problems being compensated by proximity in other dimensions as a driver of innovation, while there is no support for a potential overlap–innovation mechanism.
    Keywords: proximities; innovation; collaboration; Goldilocks principle; Norway
    JEL: J50
    Date: 2016–08–17
  3. By: Gandy, Axel; Veraart, Luitgard A. M.
    Abstract: We develop a Bayesian methodology for systemic risk assessment in financial networks such as the interbank market. Nodes represent participants in the network and weighted directed edges represent liabilities. Often, for every participant, only the total liabilities and total assets within this network are observable. However, systemic risk assessment needs the individual liabilities. We propose a model for the individual liabilities, which, following a Bayesian approach, we then condition on the observed total liabilities and assets and, potentially, on certain observed individual liabilities. We construct a Gibbs sampler to generate samples from this conditional distribution. These samples can be used in stress testing, giving probabilities for the outcomes of interest. As one application we derive default probabilities of individual banks and discuss their sensitivity with respect to prior information included to model the network. An R-package implementing the methodology is provided.
    Keywords: Financial network; unknown interbank liabilities; systemic risk; Bayes; MCMC; Gibbs sampler; power law
    JEL: F3 G3
    Date: 2016–10–06
  4. By: Wayne Yuan Gao
    Abstract: We consider an index model of dyadic link formation with a homophily effect index and a degree heterogeneity index. We provide nonparametric identification results in a single large network setting for the potentially nonparametric homophily effect function, the unobserved individual fixed effects and the unknown distribution of idiosyncratic pairwise shocks, up to normalization for each possible true value of the unknown parameters. Departing from the popular practice of restricting the norm of unknown parameters to be unity, we instead impose scale normalization on an arbitrary interquantile range, which proves particularly convenient for characterizing the identification relationships in our model, as quantiles provide direct linkages between the observable conditional probabilities and the unknown index values. We then use an inductive "in-fill" and "out-expansion" algorithm to establish our main identification results. We also provide a formal analysis of normalization that is without loss of generality in a precise sense, and discuss its implications concerning the interpretation of the results and counterfactual analyses of the model.
    Date: 2017–10
  5. By: Fabio Caccioli; Paolo Barucca; Teruyoshi Kobayashi
    Abstract: The global financial system can be represented as a large complex network in which banks, hedge funds and other financial institutions are interconnected to each other through visible and invisible financial linkages. Recently, a lot of attention has been paid to the understanding of the mechanisms that can lead to a breakdown of this network. This can happen when the existing financial links turn from being a means of risk diversification to channels for the propagation of risk across financial institutions. In this review article, we summarize recent developments in the modeling of financial systemic risk. We focus in particular on network approaches, such as models of default cascades due to bilateral exposures or to overlapping portfolios, and we also report on recent findings on the empirical structure of interbank networks. The current review provides a landscape of the newly arising interdisciplinary field lying at the intersection of several disciplines, such as network science, physics, engineering, economics, and ecology.
    Date: 2017–10

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