Research

1. Vallarino, D. (2026). “Stochastic Network Survival Dynamics: A Nonlinear Evolution Problem on Economic Graphs” Communications in Nonlinear Science and Numerical Simulation

2. Vallarino, D. (2026, in press). "Estimating Structural Spillovers in International Trade Networks: An Empirical Assessment with Graph Neural Models." Empirical Economics  

3. Vallarino, D. (2026) “Taxing the Invisible: Graph-Based Information Structures for Redistributive Design underUncertainty” Journal of Economics, Finance and Administrative Science

4. Vallarino, D. (2026). Spectral curvature of stochastic hazard operators on graphs. Statistics and Probability Letters.

5. Vallarino, D. (2026, in press). "Artificial Intelligence, Affective Well-Being and Workplace Happiness in Iberoamerican Companies" Management Research

6. Vallarino, D. (2026) “Institutional Stability Beyond Quality: Structural Coherence, Regime Persistence, and Developmental Trajectories in Latin America” Applied Journal of Economics, Law and Governance

7. Vallarino, D. (2025) "Causal-GNN for Ethical AI in Financial Services: Ensuring Fairness, Compliance, and Transparency in Automated Decision-Making" Artificial Intelligence and Law 

8. Vallarino, D. (2025) “Augmenting Trade Complexity Analysis with Deep Learning: An AI-Based Framework for Small Open Economies.” Applied Economics Letters 

—— (2025). GNN–GAN Structural Explorer: Interactive Companion to “Augmenting Trade Complexity Analysis with Deep Learning.” Shiny App (v1.0). [Available here] 

9. Vallarino, D. (2025) “Bridging the Credit Gap: How Causal AI Can Enhance Fairness and Profitability in Emerging Markets.” Global Business and Economics Review 

10. Vallarino, D. (2025) “Advancing Fraud Detection with Hybrid AI: A MoE, RNN, and Transformer-Based Approach for Financial Risk Assessment” Journal of Information Economics 

11. Vallarino, D. (2025) “When Preferences Diverge: Rethinking Rational Choice with AI-Based Economic Modeling.” Journal of Economic Analysis

12. Vallarino, D. (2025) “Decoding the Puzzle of Joblessness: Machine Learning Predicts Unemployment Trends in the Americas.” Journal of Regional Economics 4(1), 17.

13. Vallarino, D. (2025). “An AI-Enhanced Forecasting Framework: Integrating LSTM and Transformer-Based Sentiment for Stock Price Prediction”. Journal of Economic Analysis 4(3), 109

14. Vallarino, D. (2025). “Understanding the Market Trends: A Hybrid Approach to Stock Price Prediction Using RNNs and Transformer-Based Sentiment Analysis" Journal of Applied Economic Sciences, Volume XX, Spring 2025, Issue 1(87)

1. Vallarino, D. (2024). “Analyzing Economic Convergence Across the Americas: A Survival Analysis Approach to Gross Domestic Product Per capita Trajectories”. Journal of Applied Economic Sciences, Volume XIX, Summer, 2(84), 131–145.

2. Vallarino, D. (2024) “Temporal Dynamics of Countries' Journey to Cluster-Specific GDP per Capita: A Comprehensive Survival Study” Review of Economic Assessment 3(1), 1-19

3. Vallarino, D. (2024). “A Comparative Machine Learning Survival Models Analysis for Predicting Bank Failure in the US (2001-2023)”. Journal of Economic Analysis. 3(1), 129-144

4. Vallarino, D. (2024). “Machine Learning Algorithms for Survival Analysis: Advantages, Disadvantages, and examples”. International Journal of Artificial Intelligence and Machine Learning. 4(1), 10-21 

1. Vallarino, D. (2023). “Predicting Machine Learning Survival Models comparison: the case of startups time to failed with collinearity-related issues”. Journal of Economics Statistics. 1(3), 1-15.

2. Vallarino, D. (2023). “When should they buy? Surviving machine learning models for purchase timing”. International Journal of Data Mining & Knowledge Management Process vol 13, no 4/5

3. Navarro, D. V. (2007). “El comportamiento innovador como fuente del cambio: el ejemplo en las empresas de servicios”. Revista da FAE  10(1)

4. Vallarino, D. (2005). “Enterprise Innovation Model: cómo las empresas de Latinoamérica enfrentan la nueva competencia”. Revista de Antiguos Alumnos del IEEM, 8(2), 100-108

1. “Structural Hazards in Global Production: A Graph Neural Network Approach to Sectoral Survival.” Structural Change and Economic Dynamics. [Revise & Resubmit]. 

This paper models sectoral exit as a network-dependent hazard process by integrating survival analysis with graph-based representations of global production systems. Using a synthetic panel calibrated to empirical distributions and validated with WIOD data for 2000–2014, it compares standard Cox models, network-augmented specifications, embedding-based approaches, and a GNN–Cox model. The results show that sectoral exit risk depends not only on observable characteristics but also on higher-order network position, with peripheral sectors facing higher hazards and highly connected sectors displaying greater structural resilience, especially during periods of network reorganization such as 2008–2014.

2. “Stability without Transformation: Economic Discourse and Reform Capacity in Small Open Economies.” Review of Political Economy. [Revise & Resubmit]. 

This paper examines why small open economies can sustain macroeconomic stability while experiencing limited structural transformation and modest productivity growth. It argues that economic discourse operates as an endogenous informal institutional mechanism that allocates attention across policy domains, shaping how structural constraints, conflicts, and reform possibilities are framed and politically prioritized. Using Uruguay in 2021–2025 as a case and contrasting it with advanced economies, the paper introduces the concept of Institutional Attention Allocation to show how discourse may stabilize expectations while reinforcing a conservative political-economic equilibrium that limits transformation-oriented reform.

3. “Successful Investment Projects Are Contagious: Social Learning and Network Effects in Industrial Policy.” Economic Systems. [Revise & Resubmit]. 

This paper argues that fiscal incentives affect investment not only through prices, liquidity, or expected returns, but also through an informational channel. Using administrative data on promoted investment projects in Uruguay between 1974 and 2010, it shows that firms learn from previously approved projects, treating them as reference templates for subsequent applications. The evidence suggests that this learning effect is stronger under tighter credit conditions, implying that industrial policy shapes firm behavior both by altering financial incentives and by generating observable signals about how the regime is implemented in practice.

4. “Institutional Distance and Governance Performance: A Structural Analysis.” Economics of Governance. [Revise & Resubmit].

This paper develops a structural framework to assess how bureaucratic organization and institutional fit shape governance performance. Focusing on Uruguay, Paraguay, Panama, and Peru, it represents governance systems as positions in a latent bureaucratic-institutional space constructed only from institutional and organizational inputs, while keeping development outcomes as external benchmarks. The results show that reform potential depends on institutional compatibility: coherent systems exhibit smaller but more stable adjustments, whereas fragmented bureaucratic configurations display larger expected gains under deeper organizational realignment.

5. “Narrative Pacing under Fragmented Attention: A Structural Approach to Suspense.” Poetics. [Revise & Resubmit]. 

This paper develops a structural framework for analyzing narrative pacing in serialized suspense television under fragmented reception. It conceptualizes narratives as temporal cultural objects organized through non-linear sequencing, information latency, conditional surprise, and structural closure, and examines their association with persistence proxies such as early abandonment, season completion, and inter-episode delay. Using a curated corpus of contemporary suspense, crime, and mystery series, the paper shows that pacing involves structural trade-offs between surprise, reconstruction demands, delayed resolution, and discontinuation risk, offering a replicable cultural-analytic framework that does not reduce narrative value to engagement metrics.

6. “Stochastic Network Survival Dynamics: A Computational Extension of the Graph-Embedded Hazard Model.” Computational Economics. [Revise & Resubmit]. 

This paper develops a stochastic extension of the Graph-Embedded Hazard Model to study how uncertainty in network structure affects survival dynamics in interconnected economies. It formulates a coupled GEHM–PDE–SDE framework in which structural complexity evolves stochastically and node-level hazards depend on drift, volatility, and topology-dependent exposure, solved through spectral graph methods, finite differences, and Monte Carlo simulation. Numerical experiments show that structural volatility shifts complexity toward higher-risk states, amplifies exit hazards, produces heavier failure tails, and that these effects are strongest in scale-free network geometries.

1. Vallarino, D. (2026). Hall-Like Transversal Stress and Sandpile Criticality on Real Production Networks Available on arXiv

2. Vallarino, D. (2026). Sandpile Economics: Theory, Identification, and Evidence . Available on arXiv.

3. Vallarino, D. (2026). Identification and Inference in Nonlinear Dynamic Network Models. Available on arXiv. 

4. Vallarino, D. (2026). Nonlinear Fiscal Transitions and the Dynamics of Public Expenditure Reform. Available on arXiv.

5. Vallarino, D. (2026). Synthetic Firm-Level Dataset with Realistic Credit Risk and Financial Structures [Data set]. Zenodo. 

6. Vallarino, D. (2025). A Semi-Synthetic Social and Clinical Survival Dataset with Structured Dependence and Informative Censoring [Data set]. Zenodo. 

7. Vallarino, D. (2025) "The Graph-Embedded Hazard Model (GEHM): Stochastic Network Survival Dynamics on Economic Graphs"  Available on arXiv.

8. Vallarino, D. (2025) "Invited to Develop: Institutional Belonging and the Counterfactual Architecture of Developments" Available on arXiv.

9. Vallarino. D. (2025) "Data for Inclusion: The Redistributive Power of Data Economics” Available at ArXiv 

10. Vallarino, D. (2025) “Adaptive Market Intelligence: A Mixture of Experts Framework for Volatility-Sensitive Stock Forecasting” Available at ArXiv

11. Vallarino, D. (2025) “Causal GNNs and the Anthropology of Data: An Ethical Approach to Fairness and Transparency in Financial AI” Available at SSRN

12. Vallarino, D. (2025) “AI-Powered Fraud Detection in Financial Services: GNN, Compliance Challenges, and Risk Mitigation” Available at SSRN

13. Vallarino, D. (2025) “How Do Consumers Really Choose? Exposing Hidden Preferences with the Mixture of Experts Model.” Available at ArXiv

14. Vallarino, D. (2024) “Modeling Adaptive Fraud Patterns: An Agent-Centric Hybrid Framework with MoE and Deep Learning” Available at SSRN

15. Vallarino, D. (2024) “A Dynamic Approach to Stock Price Prediction: Comparing RNN and Mixture of Experts Models Across Different Volatility Profiles” Available at ArXiv

16. Vallarino, D. (2024) “Dynamic Portfolio Rebalancing: A Hybrid new Model Using GNNs and Pathfinding for Cost Efficiency” Available at ArXiv

17. Vallarino, D. (2024) “Machine Learning and Econometric Approaches to Fiscal Policies: Understanding Industrial Investment Dynamics in Uruguay (1974-2010)” Available at ArXiv

18. Vallarino, D. (2024) “Understanding the Market Trends: A Hybrid Approach to Stock Price Prediction Using RNNs and Transformer-Based Sentiment Analysis” Available at SSRN

19. Vallarino, D. (2024) “Decoding the Puzzle of Joblessness: Machine Learning Predicts Unemployment Trends in the Americas” Available at SSRN

20. Vallarino, D. (2024) “Analyzing Economic Convergence Across the Americas: A Survival Analysis Approach to GDP per Capita Trajectories” Available at arXiv

1. Vallarino, D. (2026) "Identification in Duration Models with Network-Correlated Unobservables" Econometric Society - 2026 North American Summer Meeting. (June 4 - 7, 2026, Atlanta, Georgia, US)

2. Vallarino, D. (2025) “Breaking the Product Space: Rethinking Economic Complexity with GNNs and Synthetic Trade Networks in Emerging Economies.” Conference on Economic Complexity 2025 (July 9–11, Toulouse, France). 

3. Vallarino, D. (2025). “Market Power and Tax Incentives for Sustainable Growth: A Network-Based Approach in Latin America”. BALAS 2025 Conference, (San José, Costa Rica, from April 7 to 11, 2025)

4. Vallarino, D. (2024). “Stock Behavior Using RNN Models and Transformer-Based Text Analysis”. LatinR 2024 Conference, Montevideo, Uruguay

5. Vallarino, D. (2023). “Comparative Analysis of Machine Learning Models for Survival Analysis: Empirical Study and Performance Assessment” LatinR 2023 Conference, Montevideo, Uruguay

6. Vallarino, D. (2023). “Buy when? Survival machine learning model comparison for purchase timing.” 4th International Conference on Big Data, Machine Learning and IoT (BMLI 2023) Dubai, UAE.

7. Vallarino, D., Azúa C, Úbeda, R. (2023). “Differentiated Roles and Perceptions of the Controller Function: An Exploratory Study”. Business Association of Latin America Studies (BALAS) Annual Conference, Mexico City, Mexico.