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My books reflect two complementary strands of my academic and professional journey. On one hand, I explore the frontiers of applied machine learning in economics, focusing on survival models, evaluation metrics, and empirical applications that connect econometric theory with AI-based methods. On the other, I examine the dynamics of innovation and competitiveness in Latin America, analyzing how firms in emerging markets adapt to globalization and structural change.
Together, these works bridge technical rigor with regional and institutional context, offering both methodological contributions to economic analysis and broader reflections on development and resilience in complex environments.
Vallarino, D. (2023). Machine Learning Survival Models for Economic Analysis: Inventory, Evaluation Metrics, and Empirical Analysis. Eliva Press.
This book explores the application of survival analysis models within economics, with a focus on integrating machine learning techniques into empirical research. It provides a structured inventory of methods, discusses evaluation metrics, and offers applied case studies bridging econometrics with artificial intelligence. The work positions survival models as a powerful tool for analyzing institutional change, firm dynamics, and financial risk in complex economic environments.
You can view or purchase it on Amazon here
Innovando desde el sur
Vallarino, D. (2005). Innovando desde el sur: Cómo las empresas de América Latina enfrentan la nueva competencia. Ediciones La Gotera.
Written at an earlier stage of my career, this book examines how Latin American firms adapt to the challenges of globalization and increasing international competition. It highlights strategies of innovation, resilience, and competitiveness developed by companies in the region, offering insights into the specific constraints and opportunities of emerging markets.
Beyond formal academic writing, I use my Medium page as a platform to share ideas in a more exploratory and reflective format. There I write about the implications of artificial intelligence for economics and finance, the challenges of algorithmic governance, and the lessons of institutional history for contemporary policy debates. My essays seek to make technical discussions accessible to a broader audience, while still grounded in rigorous analysis.
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