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on Network Economics |
| By: | Yang Sun; Wei Zhao; Junjie Zhou |
| Abstract: | Many widely used network centralities are based on counting walks that meet specific criteria. This paper introduces a systematic framework for walk enumeration using generating functions. We introduce a first-passage decomposition that uniquely divides any walk passing through specified nodes or links into two components: a first-reaching walk and a subsequent walk. This decomposition yields a system of interconnected equations that relate three disjoint categories of walks: unrestricted walks, walks that avoid specific elements, and walks that pass through designated sets. The framework offers a range of applications, including evaluating the effects of structural interventions, such as node or link modifications, on network walks, generalizing target centrality to multi-receiver scenarios in information networks, and comparing different strategies for adding links. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.09492 |
| By: | Alberto Baccini; Lucio Barabesi; Carlo Debernardi |
| Abstract: | This paper investigates the impact of the global financial crisis on the shape of economics as a discipline by analyzing EconLit-indexed journals from 2006 to 2020 using a multilayer network approach. We consider two types of social relationships among journals, based on shared editors (interlocking editorship) and shared authors (interlocking authorship), as well as two forms of intellectual proximity, derived from bibliographic coupling and textual similarity. These four dimensions are integrated using Similarity Network Fusion to produce a unified similarity network from which journal communities are identified. Comparing the field in 2006, 2012, and 2019 reveals a high degree of structural continuity. Our findings suggest that, despite changes in research topics after the crisis, fundamental social and intellectual relationships among journals have remained remarkably stable. Editorial networks, in particular, continue to shape hierarchies and legitimize knowledge production. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.09079 |
| By: | Arkadiusz Lipiecki; Katarzyna Sznajd-Weron |
| Abstract: | Discontinuous phase transitions in opinion dynamics are closely linked to tipping points, critical mass effects, and hysteresis, phenomena that have been confirmed empirically and recognized as highly important in social systems. The multistate q-voter model, which describes discrete decision-making where an agent selects one option from a finite set, is particularly relevant in this context. Previous studies on complete graphs uncovered a counterintuitive result: In the presence of anticonformity, the quenched formulation (traits fixed in time) yields discontinuous transitions, whereas the annealed formulation (traits reshuffled dynamically) leads to continuous transitions, contrary to the common expectation that quenched heterogeneity smooths transitions. To test whether this effect is merely a mean-field artifact, we extend the analysis to random graphs. Using pair approximation and Monte Carlo simulations, we show that the phenomenon persists beyond the complete graph, specifically on random regular graphs and Barabási–Albert scale-free networks. The novelty of our work is twofold: (i) we demonstrate for the first time that replacing annealed with quenched dynamics can alter network phase transitions from continuous to discontinuous, and (ii) we provide pair-approximation results for the multistate q voter model with competing conformity and anticonformity mechanisms, covering both quenched and annealed cases, which had previously been studied only in binary models. |
| Keywords: | Collective behavior in networks; Collective decision-making; Opinion dynamics; Agent-based modeling; Social hysteresis; Complex systems; Pair approximation |
| JEL: | C63 D72 D91 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ahh:wpaper:worms2507 |
| By: | Torben Klarl |
| Abstract: | This paper investigates the impact of creativity on technological advancement, long-term economic development, and social welfare, with creativity endogenously determined through interactions within social networks. We demonstrate that an economy remains stagnant, exhibiting neither networking nor long-term growth, when the size of the creative class falls below a certain positive threshold. Conversely, surpassing this threshold triggers active networking between creative and non-creative individuals, fostering sustained technological progress and income growth. We calibrate the model and simulate the economy’s transition from stagnation to dynamic growth. Although immediate welfare gains from transitioning to a growing economy are modest, medium- to long-term welfare improvements become substantial due to the cumulative effects of technological advancement facilitated by networking. |
| Keywords: | Creativity, Population dynamics, Innovation, Technological evolution, Endogenous growth, Network, Welfare |
| JEL: | E13 E14 I30 O11 O31 O33 |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:atv:wpaper:2504 |
| By: | Avishek Bhandari |
| Abstract: | This paper introduces a novel framework for analyzing systemic risk in financial markets through multi-scale network dynamics using Model Context Protocol (MCP) for agent communication. We develop an integrated approach that combines transfer entropy networks, agent-based modeling, and wavelet decomposition to capture information flows across temporal scales implemented in the MCPFM (Model Context Protocol Financial Markets) R package. Our methodology enables heterogeneous financial agents including high-frequency traders, market makers, institutional investors, and regulators to communicate through structured protocols while maintaining realistic market microstructure. The empirical analysis demonstrates that our multi-scale approach reveals previously hidden systemic risk patterns, with the proposed systemic risk index achieving superior early warning capabilities compared to traditional measures. The framework provides new insights for macroprudential policy design and regulatory intervention strategies. The complete implementation is available as an open-source R package at https://github.com/avishekb9/MCPFM to facilitate reproducible research and practical applications. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.08065 |
| By: | Aaron I. Plex Sulá (UF - University of Florida [Gainesville]); Valentina de Col (ICARDA - Centre international de recherche agricole dans les zones arides); Berea A. Etherton (UF - University of Florida [Gainesville]); Yanru Xing (UF - University of Florida [Gainesville]); Amogh Agarwal (UF - University of Florida [Gainesville]); Lejla Ramić (UF - University of Florida [Gainesville]); Enrico Bonaiuti (ICARDA - Centre international de recherche agricole dans les zones arides); Michael Friedmann (CIP - Centre international de la pomme de terre); Claudio Proietti (Dims - Direction de l'impact et du marketing de la science - Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement); Graham Thiele (CIP - Centre international de la pomme de terre); Karen A. Garrett (UF - University of Florida [Gainesville]) |
| Abstract: | CONTEXT: Understanding research collaboration in diverse scientific communities is key to building global agricultural research systems that support the UN Sustainable Development Goals. Characterizing collaboration patterns can inform decisions to enhance the structure and dynamics of research programs. OBJECTIVE: We introduce a new analytic framework for evaluating collaborative research networks based on scientific publications, and an associated conceptual framework for the role of research networks in achieving societal goals. We analyzed two CGIAR Research Programs: Grain Legumes and Dryland Cereals (GLDC) and Roots, Tubers and Bananas (RTB). The analysis provides a multi-dimensional perspective on a set of key questions related to research team composition, research management structures, and performance of scientific publications. METHODS: We quantified network structures of research collaborations at the level of authors, institutions, countries, and management structures, including use of temporal exponential random graph models. We used regression models to understand the associations between the characteristics of authors and publications, and the corresponding citation rates and Altmetric Attention Scores. RESULTS AND CONCLUSIONS: We identified key network hubs in the collaboration networks of both CGIAR programs. The proportion of women as authors in publications was less than a third, with a low likelihood of co-authorship between women. Institutional hubs were identified by institutional categories; these were often institutions that are considered CGIAR program "participants", and a few were "planning partners". For both GLDC and RTB, the countries that were the focus of most research coincided with the program's priority countries. Most international collaborations occurred between institutions headquartered in Global South countries, but most intercontinental collaborations occurred between Global South and Global North countries. Most institution and author co-authorships occurred in only one year and rarely lasted two or three consecutive years. High diversity in the geographic affiliations of authors, along with highly collaborative teams, as opposed to simply the number of authors, consistently were associated with more citations and higher Altmetric Attention Scores. SIGNIFICANCE: These analyses reveal key structures in research collaboration networks in GLDC and RTB research programs, with potential to guide agricultural research systems for sustainable development. Considering these outcomes from past research management can help scientists, program managers, and funders increase the success of new research projects. Specifically, future research management strategies need to fortify existing scientific capacity and development through gender parity and balanced international collaborations, working toward more impactful publications and increased development relevance, while team size increases over time. |
| Keywords: | réseau de recherche, recherche agronomique, développement agricole, innovation agricole, analyse de réseau, gestion des ressources naturelles, programme de développement, programme de recherche, Knowledge management, Network analysis, Science of science, Science mapping, Successful research networks, Web of Science, Agricultural innovation |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05182057 |
| By: | Fay\c{c}al Djebari; Kahina Mehidi; Khelifa Mazouz; Philipp Otto |
| Abstract: | This paper examines several network-based volatility models for oil prices, capturing spillovers among OPEC oil-exporting countries by embedding novel network structures into ARCH-type models. We apply a network-based log-ARCH framework that incorporates weight matrices derived from time-series clustering and model-implied distances into the conditional variance equation. These weight matrices are constructed from return data and standard multivariate GARCH model outputs (CCC, DCC, and GO-GARCH), enabling a comparative analysis of volatility transmission across specifications. Through a rolling-window forecast evaluation, the network-based models demonstrate competitive forecasting performance relative to traditional specifications and uncover intricate spillover effects. These results provide a deeper understanding of the interconnectedness within the OPEC network, with important implications for financial risk assessment, market integration, and coordinated policy among oil-producing economies. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.15046 |
| By: | Gabriel Marcos Arcanjo (Department of Economics, Federal University of Juiz de Fora, Brazil); Fernando Salgueiro Perobelli (Department of Economics, Federal University of Juiz de Fora, Brazil); Vinicius Vale (Department of Economics, Federal University of Paraná, Brazil); Douglas Silveira (Department of Economics, Federal University of Juiz de Fora, Brazil) |
| Abstract: | To understand how different regions withstand increasingly frequent exogenous shocks, we analyze regional resilience in Brazil, motivated by persistent structural inequalities and frequent exposure to such shocks. Using interregional input-output matrices for 2011 and 2019, we adopt an ex ante approach, simulating scenarios that stress the productive structure to evaluate regional responses. These results are integrated with network analysis and machine learning techniques to classify degrees of regional resilience. Resilience is highest in the South and Southeast, particularly São Paulo, and lowest in the North and Northeast. This pattern is associated with the concentration of activities with limited diffusion to less resilient regions, which exhibit growth and specialization in primary activities, along with dependence on services weakly integrated into production chains. Overall, resilience remains largely stable, highlighting structural barriers that hinder transformation and are not easily overcome spontaneously over time. |
| Keywords: | Regional Resilience; Input-Output; Network Analysis; Machine Learning |
| JEL: | C67 O40 C22 R10 R15 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ris:nereus:021497 |
| By: | Pablo D'Erasmo; Selman Erol; Guillermo Ordoñez |
| Abstract: | Recent U.S. and European regulations promote centrally clearing derivatives to reduce complexity and systemic risk in the financial system. We argue that more clearing does not guarantee less systemic risk. We identify conditions under which the core clears less intensively than the periphery, which increases systemic risk by substituting multilateral netting for bilateral netting and making contagion less likely to start in the core but more likely to spread from the core. We study confidential derivatives regulatory data and find evidence of such clearing patterns. We further explore the implications of complexity and centrality within the financial system for stability |
| Keywords: | Central Clearing; Systemic Risk; Interbank Networks; Central Counterparty (CCP); Over-the-Counter Trading (OTC) |
| JEL: | G20 E50 L14 |
| Date: | 2025–08–25 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedpwp:101515 |
| By: | Kevin Dano; Bo E. Honor\'e; Martin Weidner |
| Abstract: | This paper systematically analyzes and reviews identification strategies for binary choice logit models with fixed effects in panel and network data settings. We examine both static and dynamic models with general fixed-effect structures, including individual effects, time trends, and two-way or dyadic effects. A key challenge is the incidental parameter problem, which arises from the increasing number of fixed effects as the sample size grows. We explore two main strategies for eliminating nuisance parameters: conditional likelihood methods, which remove fixed effects by conditioning on sufficient statistics, and moment-based methods, which derive fixed-effect-free moment conditions. We demonstrate how these approaches apply to a variety of models, summarizing key findings from the literature while also presenting new examples and new results. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.11556 |
| By: | Mengting Li (School of Finance, Nanjing University of Finance and Economics, Nanjing 210023, China); Yu Wei (School of Finance, Yunnan University of Finance and Economics, Kunming 650221, China); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Oguzhan Cepni (Ostim Technical University, Ankara, Turkiye; University of Edinburgh Business School, Centre for Business, Climate Change, and Sustainability; Department of Economics, Copenhagen Business School, Denmark) |
| Abstract: | This paper investigates the dynamic risk spillovers and frequency-domain connectedness between climate-related financial risk, traditional market volatility, and key macroeconomic variables in the overall European Union region. Employing a novel Mixed-Frequency Vector Autoregression with Frequency Domain Decomposition (MF-VAR-FDD) model, we analyze a unique dataset comprising weekly volatility indices for carbon (Carbon VIX), gold (Gold VIX), oil (Oil VIX), and equities (Euro VIX), alongside monthly data for industrial production, the ECB's shadow short rate, and inflation. This methodology allows for a nuanced decomposition of risk transmission across short-term (high-frequency) and long-term (low-frequency) horizons, providing critical insights that are obscured in common-frequency analyses. Our empirical results reveal a distinct asymmetry in the risk network: financial and commodity volatility indicators consistently act as net transmitters of risk, whereas macroeconomic fundamentals are systemic net receivers. The total spillover index is highly time-variant, exhibiting significant spikes that coincide with major economic and geopolitical events. The frequency decomposition further demonstrates that high-frequency (0-3 months) spillovers are predominantly driven by interactions within financial markets, with the Euro VIX playing a central role. Conversely, low-frequency (beyond 3 months) spillovers are more structural, with commodity price volatility (Oil VIX) and monetary policy expectations (ECB SSR) emerging as the largest long-term risk transmitter and receiver, respectively. More importantly, we find the prominent and pervasive role of the Carbon VIX as a source of systemic risk. Across the full sample, the Carbon VIX emerges as the most powerful net risk transmitter, indicating that volatility originating from the carbon market significantly propagates throughout the financial and macroeconomic system. Our findings, robust to alternative model specifications, underscore the imperative for policymakers and investors to integrate carbon market dynamics into their risk management frameworks and highlight the inadequacy of traditional models that ignore mixed-frequency information. |
| Keywords: | Carbon price uncertainty, Macroeconomy, Mixed-frequency Spillover, Time- and Frequency Domain |
| JEL: | C32 E30 E44 G10 Q02 Q54 |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202527 |