A digital illustration representing the intersection of AI and economics, featuring a neural network brain, economic data points, and the Phillips Curve.

Decoding the Economy: Can AI Bridge the Output Gap?

Elliot Brynn in Business & Economy August 2025 • 4 min read.

"A cutting-edge neural network model offers fresh insights into inflation and economic activity, challenging traditional Phillips Curve analysis."

The Phillips Curve, a cornerstone of modern macroeconomics, has long been plagued by challenges. Empirical models struggle to accurately capture the relationship between inflation and economic activity, hindered by unobservable factors like inflation expectations and the elusive output gap. This has led to debates about the effectiveness of current monetary policies.

Traditional approaches rely on proxies and assumption-heavy filtering techniques to address these gaps. However, a new approach is emerging: the Hemisphere Neural Network (HNN), a machine-learning model designed for economically interpretable inflation predictions. The HNN offers an alternative route, promising more accurate forecasts and deeper insights into the forces driving inflation.

This article explores the innovative architecture and capabilities of the HNN, highlighting its potential to revolutionize our understanding of the Phillips Curve and macroeconomic dynamics. We'll delve into how the HNN addresses the limitations of traditional models, offering a fresh perspective on the interplay between inflation, economic activity, and monetary policy.

What is the Hemisphere Neural Network (HNN)?

A digital illustration representing the intersection of AI and economics, featuring a neural network brain, economic data points, and the Phillips Curve.

The Hemisphere Neural Network (HNN) is a novel artificial intelligence model designed to overcome the limitations of traditional Phillips Curve analysis. Unlike conventional methods that rely on proxies for unobserved variables, the HNN takes a different approach: it directly estimates the latent states within a Neural PC, supervised by a deep learning model that maps observed regressors into hidden latent states.

Here's how the HNN works:

Nonlinearity Capture: The HNN excels at capturing the complex, nonlinear relationships between economic indicators and latent states. It translates a high-dimensional set of observed data into meaningful economic indicators, addressing the limitations of linear models.
Economic Interpretability: The architecture of the HNN is designed to yield a final layer of components that can be interpreted as latent states within a Neural Phillips Curve. The unique architecture allows for economic interpretation of the results, a crucial feature often lacking in black-box machine learning models.
Improved Forecasting: HNN forecasts show better performance than traditional PC-based models, particularly in capturing recent economic shifts like the 2021 inflation upswing. HNN attributes this upswing to a large positive output gap starting from late 2020, a conclusion supported by its analysis of alternative tightness indicators.

By dispensing with traditional measures like unemployment and GDP in favor of a nonlinearly processed amalgam of tightness indicators, the HNN offers a unique perspective on the output gap and its relationship to inflation.

The Future of Economic Modeling with AI

The Hemisphere Neural Network represents a significant step forward in macroeconomic modeling. By leveraging the power of AI and machine learning, the HNN offers a more nuanced and accurate understanding of the complex relationships driving inflation and economic activity. This innovative approach has the potential to improve forecasting, inform monetary policy decisions, and address key issues in empirical macroeconomic analysis. Further exploration and refinement of models like the HNN promise to unlock even deeper insights into the workings of the economy.

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: https://doi.org/10.48550/arXiv.2202.04146,

Title: A Neural Phillips Curve And A Deep Output Gap

Subject: econ.em stat.ap stat.ml

Authors: Philippe Goulet Coulombe

Published: 08-02-2022

Everything You Need To Know

What is the Hemisphere Neural Network (HNN) and how does it differ from traditional economic models?

The Hemisphere Neural Network (HNN) is a novel artificial intelligence model designed to analyze macroeconomic relationships, particularly those concerning inflation and economic activity. Unlike traditional models, which often rely on linear assumptions and proxies for unobservable factors, the HNN employs a deep learning approach to directly estimate latent states within a Neural Phillips Curve. This enables the HNN to capture complex, nonlinear relationships between economic indicators, providing more accurate forecasts and offering deeper insights into the drivers of inflation, like the output gap. It addresses limitations of conventional methods which struggle to accurately capture the relationship between inflation and economic activity, hindered by unobservable factors like inflation expectations and the elusive output gap.

How does the HNN address the challenges associated with the Phillips Curve in economic analysis?

The HNN addresses the challenges associated with the Phillips Curve by moving beyond traditional methods that struggle with unobservable factors and rely on proxies. By using a deep learning model, the HNN directly estimates the latent states within a Neural Phillips Curve. The HNN focuses on a nonlinearly processed amalgam of tightness indicators to offer a fresh perspective on the output gap and its relationship to inflation. This approach allows the HNN to provide more accurate forecasts, interpret complex economic dynamics and offer a more nuanced understanding of how economic activity impacts inflation.

What are the key capabilities of the Hemisphere Neural Network (HNN) that make it an innovative tool for economic analysis?

The HNN has several key capabilities that make it an innovative tool. First, it excels at capturing complex, nonlinear relationships between economic indicators and latent states. Second, the architecture of the HNN allows for economic interpretation of the results, a crucial feature often lacking in black-box machine learning models. Finally, HNN forecasts show better performance than traditional PC-based models, particularly in capturing recent economic shifts. For instance, the HNN attributed the 2021 inflation upswing to a large positive output gap starting from late 2020.

How does the HNN provide a new perspective on the output gap?

The HNN provides a new perspective on the output gap by dispensing with traditional measures like unemployment and GDP in favor of a nonlinearly processed amalgam of tightness indicators. The HNN uses a deep learning model to derive an economic interpretation of the results. This approach enables the HNN to more accurately estimate the output gap and its relationship to inflation, offering a more nuanced understanding of how it impacts economic activity. The HNN identified a large positive output gap starting from late 2020 as a key factor in the 2021 inflation upswing, which traditional models struggle to explain.

What is the potential impact of the Hemisphere Neural Network (HNN) on monetary policy and economic forecasting?

The Hemisphere Neural Network (HNN) has the potential to significantly impact monetary policy and economic forecasting. By offering more accurate forecasts and deeper insights into the drivers of inflation and economic activity, the HNN can inform better-informed monetary policy decisions. The ability of the HNN to capture the nonlinear relationships in the economy and interpret complex data can help policymakers to respond more effectively to economic shifts and challenges. Improved forecasting capabilities could lead to more proactive and effective monetary policy interventions. Furthermore, the innovative approach of the HNN can revolutionize our understanding of the Phillips Curve and other key macroeconomic relationships, paving the way for more sophisticated economic analysis.