nep-net New Economics Papers
on Network Economics
Issue of 2023‒09‒04
eight papers chosen by
Alfonso Rosa García, Universidad de Murcia

  1. Game theoretic foundations of the Gately power measure for directed networks By Robert P. Gilles; Lina Mallozzi
  2. Control and Spread of Contagion in Networks By John Higgins; Tarun Sabarwal
  3. Peer effects and endogenous social interactions By Koen Jochmans
  4. A new mapping of technological interdependence By A. Fronzetti Colladon; B. Guardabascio; F. Venturini
  5. The Network Gravity of Global Banking By Minetti, Raoul; Romanini, Giacomo; Ziv, Oren
  6. Autoregressive networks By Jiang, Binyan; Li, Jialiang; Yao, Qiwei
  7. Randomization Inference of Heterogeneous Treatment Effects under Network Interference By Julius Owusu
  8. Amortized neural networks for agent-based model forecasting By Denis Koshelev; Alexey Ponomarenko; Sergei Seleznev

  1. By: Robert P. Gilles; Lina Mallozzi
    Abstract: We introduce a new network centrality measure founded on the Gately value for cooperative games with transferable utilities. A directed network is interpreted as representing control or authority relations between players--constituting a hierarchical network. The power distribution of a hierarchical network can be represented through a TU-game. We investigate the properties of this TU-representation and investigate the Gately value of the TU-representation resulting in the Gately power measure. We establish when the Gately measure is a Core power gauge, investigate the relationship of the Gately with the $\beta$-measure, and construct an axiomatisation of the Gately measure.
    Date: 2023–08
  2. By: John Higgins; Tarun Sabarwal
    Abstract: We study proliferation of an action in binary action network coordination games that are generalized to include global effects. This captures important aspects of proliferation of a particular action or narrative in online social networks, providing a basis to understand their impact on societal outcomes. Our model naturally captures complementarities among starting sets, network resilience, and global effects, and highlights interdependence in channels through which contagion spreads. We present new, natural, and computationally tractable algorithms to define and compute equilibrium objects that facilitate the general study of contagion in networks and prove their theoretical properties. Our algorithms are easy to implement and help to quantify relationships previously inaccessible due to computational intractability. Using these algorithms, we study the spread of contagion in scale-free networks with 1, 000 players using millions of Monte Carlo simulations. Our analysis provides quantitative and qualitative insight into the design of policies to control or spread contagion in networks. The scope of application is enlarged given the many other situations across different fields that may be modeled using this framework.
    Date: 2023–07
  3. By: Koen Jochmans (TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)
    Abstract: This paper proposes a solution to the problem of the self-selection of peers in the linear-in-means model. We do not require to specify a model for how the selection of peers comes about. Rather, we exploit two restrictions that are inherent in many such specifications to construct conditional moment conditions. The restrictions in question are that link decisions that involve a given individual are not all independent of one another, but that they are independent of the link decisions made between other pairs of individuals that are located sufficiently far away in the network. These conditions imply that instrumental variables can be constructed from leave-own-out networks.
    Keywords: Instrumental variable, Linear-in-means model, Network, Self-selection
    Date: 2023
  4. By: A. Fronzetti Colladon; B. Guardabascio; F. Venturini
    Abstract: Which technological linkages affect the sector's ability to innovate? How do these effects transmit through the technology space? This paper answers these two key questions using novel methods of text mining and network analysis. We examine technological interdependence across sectors over a period of half a century (from 1976 to 2021) by analyzing the text of 6.5 million patents granted by the United States Patent and Trademark Office (USPTO), and applying network analysis to uncover the full spectrum of linkages existing across technology areas. We demonstrate that patent text contains a wealth of information often not captured by traditional innovation metrics, such as patent citations. By using network analysis, we document that indirect linkages are as important as direct connections and that the former would remain mostly hidden using more traditional measures of indirect linkages, such as the Leontief inverse matrix. Finally, based on an impulse-response analysis, we illustrate how technological shocks transmit through the technology (network-based) space, affecting the innovation capacity of the sectors.
    Date: 2023–07
  5. By: Minetti, Raoul (Michigan State University, Department of Economics); Romanini, Giacomo (Bank of Italy); Ziv, Oren (Michigan State University, Department of Economics)
    Abstract: A substantial fraction of international banking is intermediated through banking hubs and complex multi-national routing. These flows are ignored or unaccounted for, both theoretically and empirically. We develop an N-country DSGE model of lending where banks choose the path of lending through a network of partner institutions in multiple countries. Banking hub countries arise endogenously as central nodes in the intermediation network. The model provides a framework to rationalize observable international statistics with theoretical models of banking gravity. It generates a set of bilateral locational flow of funds that conceptually matches aggregate (BIS LBS) statistics, as distinct from the ultimate demand and supply of lending. Using a series of calibrations for both node and edge shocks, we show that accounting for the network is crucial for understanding the propagation of shocks and the impact of banking consolidation on aggregate fluctuations. The analysis reveals that neglecting the multinational banking network can lead to biased conclusions about the aggregate effects of banking unions.
    Keywords: Heterogeneous Banks; Gravity; Intermediation Network; Contagion
    JEL: F40 G20
    Date: 2023–08–17
  6. By: Jiang, Binyan; Li, Jialiang; Yao, Qiwei
    Abstract: We propose a rst-order autoregressive (i.e. AR(1)) model for dynamic network processes in which edges change over time while nodes remain unchanged. The model depicts the dynamic changes explicitly. It also facilitates simple and ecient statistical inference methods including a permutation test for diagnostic checking for the tted network models. The proposed model can be applied to the network processes with various underlying structures but with independent edges. As an illustration, an AR(1) stochastic block model has been investigated in depth, which characterizes the latent communities by the transition probabilities over time. This leads to a new and more eective spectral clustering algorithm for identifying the latent communities. We have derived a nite sample condition under which the perfect recovery of the community structure can be achieved by the newly dened spectral clustering algorithm. Furthermore the inference for a change point is incorporated into the AR(1) stochastic block model to cater for possible structure changes. We have derived the explicit error rates for the maximum likelihood estimator of the change-point. Application with three real data sets illustrates both relevance and usefulness of the proposed AR(1) models and the associate inference methods.
    Keywords: AR(1) networks; change point; dynamic stochastic block model; Hamming distance; maximum likelihood estimation; spectral clustering algorithm; Yule-Walker equation
    JEL: C1
    Date: 2023–08–15
  7. By: Julius Owusu
    Abstract: We design randomization tests of heterogeneous treatment effects when units interact on a network. Our modeling strategy allows network interference into the potential outcomes framework using the concept of network exposure mapping. We consider three null hypotheses that represent different notions of homogeneous treatment effects, but due to nuisance parameters and the multiplicity of potential outcomes, the hypotheses are not sharp. To address the issue of multiple potential outcomes, we propose a conditional randomization inference method that expands on existing methods. Additionally, we propose two techniques that overcome the nuisance parameter issue. We show that our conditional randomization inference method, combined with either of the proposed techniques for handling nuisance parameters, produces asymptotically valid p-values. We illustrate the testing procedures on a network data set and the results of a Monte Carlo study are also presented.
    Date: 2023–07
  8. By: Denis Koshelev; Alexey Ponomarenko; Sergei Seleznev
    Abstract: In this paper, we propose a new procedure for unconditional and conditional forecasting in agent-based models. The proposed algorithm is based on the application of amortized neural networks and consists of two steps. The first step simulates artificial datasets from the model. In the second step, a neural network is trained to predict the future values of the variables using the history of observations. The main advantage of the proposed algorithm is its speed. This is due to the fact that, after the training procedure, it can be used to yield predictions for almost any data without additional simulations or the re-estimation of the neural network
    Date: 2023–08

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