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on Network Economics |
| By: | Antonio Cabrales; Piero Gottardi |
| Abstract: | We study a new model to study the effect of contract externalities that arise through shock transmission. We model a financial network where good firms enjoy direct and indirect benefits from linking with one another. Bad risks benefit from having a connection with a good firm, but they are a cost to both direct and indirect connections. In efficient networks the good risks should form large connected components with very few bad risks attached. The equilibrium networks, on the other hand, have many more bad risks attached, they are core-periphery structures, and components are also smaller than the efficient ones. We also study extensions with heterogenous “bad risks, ” with diversity in the costs to good risk firms of linking with bad risks, and with incomplete information. |
| Keywords: | network formation, financial shocks, financial contagion, core periphery, efficiency and equilibrium |
| JEL: | D85 G21 G32 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_11122&r= |
| By: | Houndetoungan, Aristide (Université de Paris); Kouame, Cristelle (World Bank); Vlassopoulos, Michael (University of Southampton) |
| Abstract: | Peer influence on effort devoted to some activity is often studied using proxy variables when actual effort is unobserved. For instance, in education, academic effort is often proxied by GPA. We propose an alternative approach that circumvents this approximation. Our framework distinguishes unobserved shocks to GPA that do not affect effort from preference shocks that do affect effort levels. We show that peer effects estimates obtained using our approach can differ significantly from classical estimates (where effort is approximated) if the network includes isolated students. Applying our approach to data on high school students in the United States, we find that peer effect estimates relying on GPA as a proxy for effort are 40% lower than those obtained using our approach. |
| Keywords: | social networks, peer effects, academic achievement, unobserved effort, isolated agents |
| JEL: | C31 J24 |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp16998&r= |
| By: | Ge, S.; Li, S.; Linton, O. B.; Liu, W.; Su, W. |
| Abstract: | In this paper, we propose two novel frameworks to incorporate auxiliary information about connectivity among entities (i.e., network information) into the estimation of large covariance matrices. The current literature either completely ignores this kind of network information (e.g., thresholding and shrinkage) or utilizes some simple network structure under very restrictive settings (e.g., banding). In the era of big data, we can easily get access to auxiliary information about the complex connectivity structure among entities. Depending on the features of the auxiliary network information at hand and the structure of the covariance matrix, we provide two different frameworks correspondingly —the Network Guided Thresholding and the Network Guided Banding. We show that both Network Guided estimators have optimal convergence rates over a larger class of sparse covariance matrix. Simulation studies demonstrate that they generally outperform other pure statistical methods, especially when the true covariance matrix is sparse, and the auxiliary network contains genuine information. Empirically, we apply our method to the estimation of the covariance matrix with the help of many financial linkage data of asset returns to attain the global minimum variance (GMV) portfolio. |
| Keywords: | Banding, Big Data, Large Covariance Matrix, Network, Thresholding |
| JEL: | C13 C58 G11 |
| Date: | 2024–05–20 |
| URL: | http://d.repec.org/n?u=RePEc:cam:camdae:2427&r= |
| By: | Yaxin Pang (CGS i3); Shenle Pan (CGS i3); Eric Ballot (CGS i3) |
| Abstract: | Supply chain resilience analysis aims to identify the critical elements in the supply chain, measure its reliability, and analyze solutions for improving vulnerabilities. While extensive methods like stochastic approaches have been dominant, robust optimization-widely applied in robust planning under uncertainties without specific probability distributions-remains relatively underexplored for this research problem. This paper employs robust optimization with budget-of-uncertainty as a tool to analyze the resilience of multi-modal logistics service networks under time uncertainty. We examine the interactive effects of three critical factors: network size, disruption scale, disruption degree. The computational experiments offer valuable managerial insights for practitioners and researchers. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.12565&r= |
| By: | Sandor Juhasz; Zoltan Elekes; Virag Ilyes; Frank Neffke |
| Abstract: | Strong local clusters help firms compete on global markets. One explanation for this is that firms benefit from locating close to their suppliers and customers. However, the emergence of global supply chains shows that physical proximity is not necessarily a prerequisite to successfully manage customer-supplier relations anymore. This raises the question when firms need to colocate in value chains and when they can coordinate over longer distances. We hypothesize that one important aspect is the extent to which supply chain partners exchange not just goods but also know-how. To test this, we build on an expanding literature that studies the drivers of industrial coagglomeration to analyze when supply chain connections lead firms to colocation. We exploit detailed micro-data for the Hungarian economy between 2015 and 2017, linking firm registries, employer-employee matched data and firm-to-firm transaction data from value-added tax records. This allows us to observe colocation, labor flows and value chain connec- tions at the level of firms, as well as construct aggregated coagglomeration patterns, skill relatedness and input-output connections between pairs of industries. We show that supply chains are more likely to support coagglomeration when the industries in- volved are also skill related. That is, input-output and labor market channels reinforce each other, but supplier connections only matter for colocation when industries have similar labor requirements, suggesting that they employ similar types of know-how. We corroborate this finding by analyzing the interactions between firms, showing that supplier relations are more geographically constrained between companies that operate in skill related industries. |
| Keywords: | coagglomeration, labor flow network, skill relatedness, supply chain |
| JEL: | R12 J24 O14 D57 |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:egu:wpaper:2416&r= |
| By: | Simionescu, Mihaela; Schneider, Nicolas |
| Abstract: | Understanding the structure and properties of production networks is essential to identify the transmission channels from monetary shocks. While growingly studied, this literature keeps displaying critical caveats from which the investigation of G-7 economies is not spared. To fill this gap, this paper applies a version of Time-Varying Parameters Bayesian Vector-Autoregressions models (TVP-VAR) and investigates the responses of production networks (upstream and downstream dynamics) to endogeneous monetary shocks on key macro-level indicators (GDP, GDP deflator, exchange rate, short-term and long-term interest rates). Two distinct time-lengths are considered: a test (i.e., 2000–2014) and a treated period (i.e., 2007–2009, ”the Great Recession”). Prior, key statistical conditions are checked using a stepwise stationary testing framework including the Kwiatkowski–Phillips–Schmidt–Shin (Kapetanios et al. in J Economet 112(2):359–379, 2003—KPSS) and panel Breitung (Nonstationary panels, panel cointegration, and dynamic panels. Emerald Group Publishing Limited, London, 2001) unit root tests; followed by the Pesaran (General diagnostic tests for cross section dependence in panels, 2004) Cross-sectional Dependence (CD) test; and the Im–Pesaran–Shin (Im et al. in J Economet 115(1):53–74, 2003—IPS) test for unit root in the presence of heterogenous slope coefficients. Panel Auto-Regressive Distributed Lag Mean Group estimates (PARDL-MG) offer interesting short- and long-run monetary shocks-production networks response functions, stratified by country and sector. Findings clearly indicate that upstreamness forces dominated downstremness dynamics during the period 2000–2014, whereas the financial sector ermeges as the clear transmission channel through which monetary shocks affected the productive economy during the Great Recession. In general, we conclude that the prioduction structure influences the transmission of monetary shocks in the G-7 economies. Adequate policy implications are supplied, along with a methodological note on the forecasting potential of TVP-VAR methodologies when dealing with series exhibiting structural breaks. |
| JEL: | L81 N0 |
| Date: | 2023–11–26 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:123040&r= |
| By: | Yu Cheng; Qin Yang; Liyang Wang; Ao Xiang; Jingyu Zhang |
| Abstract: | In the realm of globalized financial markets, commercial banks are confronted with an escalating magnitude of credit risk, thereby imposing heightened requisites upon the security of bank assets and financial stability. This study harnesses advanced neural network techniques, notably the Backpropagation (BP) neural network, to pioneer a novel model for preempting credit risk in commercial banks. The discourse initially scrutinizes conventional financial risk preemptive models, such as ARMA, ARCH, and Logistic regression models, critically analyzing their real-world applications. Subsequently, the exposition elaborates on the construction process of the BP neural network model, encompassing network architecture design, activation function selection, parameter initialization, and objective function construction. Through comparative analysis, the superiority of neural network models in preempting credit risk in commercial banks is elucidated. The experimental segment selects specific bank data, validating the model's predictive accuracy and practicality. Research findings evince that this model efficaciously enhances the foresight and precision of credit risk management. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.10762&r= |