A digital illustration representing the interconnectedness of global trade routes.

Decoding Global Trade: Can New Network Models Predict Economic Relationships?

Samir D’Costa in Business & Economy December 2025 • 4 min read.

"Econometric models meet maximum entropy: Exploring how cutting-edge network science is reshaping our understanding of international trade and economic forecasting."

The rise of network science has revolutionized how we understand complex systems, from social connections to biological interactions. In economics, this has translated into a growing interest in mapping and modeling international trade networks, often called the World Trade Web (WTW). By analyzing the relationships between countries through import and export data, researchers aim to uncover patterns and predict future economic activity.

Traditionally, economists have used econometric models like the Gravity Model (GM) to explain trade flows. The GM predicts that trade volume between two countries is proportional to their economic size (GDP) and inversely proportional to the distance between them. While effective, the traditional GM has limitations, particularly in capturing the complex network properties of the WTW, such as varying degree distributions and clustering patterns.

Newer approaches leverage statistical physics and the Maximum Entropy Principle (MEP) to build network models that incorporate real-world constraints and maximize the unbiasedness of the network structure. This article explores how these two approaches – econometrics and maximum-entropy modeling – are being integrated to provide a more comprehensive understanding of international trade.

Beyond Gravity: Why Traditional Trade Models Fall Short

A digital illustration representing the interconnectedness of global trade routes.

The Gravity Model, while foundational, operates on a rather simplified premise. It predicts a trade relationship between virtually all countries, failing to account for the many 'missing links' observed in the real world. This is because the GM typically produces an expected trade value that is non-zero for every country pair, which contradicts empirical evidence showing a sparse and topologically rich WTW.

To address this, economists have begun 'dressing' the GM with probability distributions that allow for zero-valued trade outcomes. This involves interpreting the GM as the expected value of a probability distribution, where the functional form of this distribution needs to be carefully determined. This has led to the development of zero-inflated and hurdle models, each designed to better account for the prevalence of missing trade links.

Zero-Inflated Models: Account for scenarios where a trade partnership exists but the actual trade volume is zero, possibly due to limited trading capacity or statistical noise.
Hurdle Models: Predict zero trade volumes as a first step, using logit or probit estimations to determine the presence of trade links before estimating the volume.

Network science offers an alternative approach. By applying the Maximum Entropy Principle (MEP), researchers construct network ensembles that maximize Shannon entropy subject to certain structural constraints. This method yields maximally unbiased network distributions compatible with observed network properties, effectively addressing the limitations of traditional econometric models and opening new avenues for understanding the WTW.

The Future of Trade Prediction: Integrating Disciplines

By combining the strengths of both econometrics and network science, researchers are developing more robust and realistic models of international trade. These integrated approaches not only improve our ability to predict trade flows but also offer valuable insights into the underlying mechanisms driving global economic relationships. As the world becomes increasingly interconnected, these advanced modeling techniques will play a crucial role in shaping trade policies and fostering sustainable economic growth.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1016/j.chaos.2022.112958,

Title: Reconciling Econometrics With Continuous Maximum-Entropy Network Models

Subject: physics.soc-ph cond-mat.dis-nn econ.em physics.app-ph stat.me

Authors: Marzio Di Vece, Diego Garlaschelli, Tiziano Squartini

Published: 03-10-2022

Everything You Need To Know

What is the Gravity Model (GM) and what are its limitations in the context of international trade?

The Gravity Model (GM) is a traditional econometric model used to predict trade flows between countries. It posits that trade volume is directly proportional to the economic sizes (GDP) of the countries and inversely proportional to the distance between them. However, the GM has limitations. It often fails to accurately represent the World Trade Web (WTW) due to its inability to capture the complex network properties, such as varying degree distributions and clustering patterns, and the prevalence of 'missing links' in real-world trade data. The GM typically predicts non-zero trade between all countries, which contradicts empirical evidence of sparse and complex trade relationships. This is why economists are exploring other methods.

How do zero-inflated and hurdle models improve upon the traditional Gravity Model?

Zero-inflated and hurdle models are advanced statistical techniques designed to address the limitations of the Gravity Model (GM). The GM often struggles with accurately representing the sparse nature of the World Trade Web (WTW), where many country pairs have no trade. Zero-inflated models account for situations where a trade partnership might exist, but the actual trade volume is zero due to factors like limited trading capacity or statistical noise. Hurdle models take a different approach by first predicting whether a trade link exists using methods like logit or probit estimations and then, if a link is predicted, estimating the trade volume. These models help to create more realistic trade predictions by accounting for the prevalence of missing trade links, a significant challenge for the traditional GM.

What role does the Maximum Entropy Principle (MEP) play in understanding international trade networks?

The Maximum Entropy Principle (MEP) is a method used in network science to construct network models that maximize Shannon entropy, subject to observed network properties. When applied to the World Trade Web (WTW), the MEP allows researchers to build network ensembles that are maximally unbiased and compatible with real-world constraints. This approach is crucial because it addresses limitations of traditional econometric models. By using the MEP, researchers can create more robust and realistic models of international trade, improving the understanding of global economic relationships and predict trade flows, especially when it comes to understanding complex network properties and addressing the 'missing links' problem inherent in the WTW.

How can integrating econometrics and network science enhance our ability to predict global trade patterns?

Integrating econometrics with network science offers a more comprehensive approach to predicting global trade patterns. Econometric models, like the Gravity Model (GM), provide a foundational understanding of trade flows based on factors like economic size and distance. Network science, particularly through the use of the Maximum Entropy Principle (MEP), allows researchers to model the complex relationships within the World Trade Web (WTW). By combining these two approaches, researchers can leverage the strengths of both. This integrated approach improves the ability to predict trade flows more accurately, and also offers insights into the underlying mechanisms driving global economic relationships. This synergy allows for the development of more robust and realistic models that can adapt to the increasing interconnectedness of the global economy.

In what ways are these new network models likely to influence international trade policies and economic forecasting?

The advancements in network modeling are poised to significantly influence international trade policies and economic forecasting. These models, by integrating econometrics and network science, offer more accurate predictions of trade flows and a deeper understanding of the underlying economic relationships. The ability to model the World Trade Web (WTW) more realistically allows policymakers to assess the impact of trade agreements, tariffs, and other policy interventions more effectively. For economic forecasting, these models can improve the accuracy of predictions about global economic trends, aiding in strategic planning and risk management. As the world becomes increasingly interconnected, these advanced modeling techniques will play a crucial role in shaping trade policies, fostering sustainable economic growth, and helping to navigate the complexities of the global economy.