Surreal illustration of a skewed graph weather vane against a stormy sky, symbolizing economic uncertainty.

Decoding Economic Uncertainty: How 'Skewness' Can Help Predict Market Swings

Beau Callahan in Business & Economy August 2025 • 3 min read.

"A fresh approach to real-time economic forecasting uses the shape of data, not just the numbers themselves, to better anticipate risk and uncertainty."

In today's rapidly changing economic landscape, traditional methods of forecasting gross domestic product (GDP) growth are often insufficient. These models typically rely on first moments of data, such as means, which may overlook critical information about potential risks. Recent economic fluctuations have exposed the limitations, particularly in communicating the uncertainty inherent in economic predictions.

A new approach is gaining traction that incorporates not only the central tendency of data but also its shape and spread. This method extends the idea of common factors to conditional dispersion (variance) and shape (asymmetry, or skewness), enhancing measures of nowcasting uncertainty and improving precision when macroeconomic instability peaks.

This innovative technique uses a flexible skewed-t model where location, dispersion, and asymmetry parameters across different economic series follow parallel factor models. By exploiting higher moments of real-time data, this approach promises a more nuanced understanding of economic trends, offering valuable insights for policymakers and economic agents alike.

The Skewed-T Model: A Deep Dive

Surreal illustration of a skewed graph weather vane against a stormy sky, symbolizing economic uncertainty.

To effectively capture the nuances of real-time economic data, a skewed-t model is employed. This model is specifically designed to measure location, scale, and shape common factors within a nowcasting framework. By using score-driven techniques, unobserved components, such as secular trends and common autoregressive components, can be modeled effectively.

The model builds upon existing methodologies, extending the univariate skewed-t model to real-time common factor analysis. Unlike traditional non-Gaussian state-space models, this approach offers a likelihood function generally available in closed form, simplifying the estimation process.

Location (μ_i,t): Represents the central tendency of the data series.
Scale (σ_i,t): Controls the dispersion of the predictive density, similar to general uncertainty.
Shape (α_i,t): Reflects the asymmetry or skewness, indicating downside and upside risks.

Each time-varying parameter is split into a trend and a common component, allowing for a more refined analysis of economic activity. The exponential function ensures scale parameters remain positive, while the hyperbolic tangent function constrains shape parameters within a specific range.

Implications and the Future of Economic Forecasting

The ability to model and interpret skewness in real-time economic data marks a significant advancement in economic forecasting. By providing a more reliable measure of uncertainty and improving precision during times of macroeconomic instability, this approach offers valuable insights for policymakers, investors, and economic analysts. As data availability and computational power continue to grow, the use of sophisticated models like the skewed-t model will likely become increasingly prevalent, enhancing our ability to navigate the complexities of the global economy.

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This article is based on research published under:

DOI-LINK: 10.1016/j.ijforecast.2024.05.003,

Title: Asymmetric Uncertainty : Nowcasting Using Skewness In Real-Time Data

Subject: econ.em

Authors: Paul Labonne

Published: 04-12-2020

Everything You Need To Know

What is 'skewness' and how does it relate to economic forecasting?

Skewness, in the context of economic forecasting, is a measure of asymmetry in a data distribution, indicating the presence of potential downside or upside risks. It's a crucial component of a new forecasting approach that goes beyond traditional methods, which often focus on the mean. By incorporating skewness, the methodology provides a more nuanced understanding of market volatility and helps to better anticipate risk and uncertainty in real-time economic predictions. The incorporation of shape, as captured by the 'skewness', enhances the accuracy of predictions, especially during periods of macroeconomic instability.

How does the skewed-t model work in capturing economic trends?

The skewed-t model is a sophisticated tool used to analyze real-time economic data. It measures three key components: location (μi,t), scale (σi,t), and shape (αi,t). The location represents the central tendency, the scale controls the dispersion or general uncertainty, and the shape, specifically the skewness, reveals any asymmetry in the data, highlighting potential downside and upside risks. This model extends the univariate skewed-t model to real-time common factor analysis, incorporating score-driven techniques to model unobserved components like trends and common autoregressive factors. This design allows for a more refined analysis of economic activity, offering valuable insights for policymakers and economic agents alike.

Why are traditional forecasting methods insufficient in today's economy?

Traditional forecasting methods often rely on the first moments of data, such as means, which can overlook critical information about potential risks and the shape of the data. These models frequently fail to capture the full range of economic uncertainties, especially during times of volatility and rapid change. Recent economic fluctuations have highlighted the limitations of these approaches in communicating the inherent uncertainty in economic predictions. By contrast, the new approach considers the shape and spread of data, including skewness, leading to improved precision and a more comprehensive understanding of market dynamics.

Can you explain the practical implications of using the skewed-t model for economic forecasting?

The practical implications of using the skewed-t model are significant. By incorporating location, scale, and shape, the model offers a more reliable measure of uncertainty and enhances the precision of economic forecasts, particularly during periods of macroeconomic instability. This translates into more informed decision-making for policymakers, investors, and economic analysts. They can better assess the risks associated with economic trends and anticipate potential market swings. Furthermore, this model allows for a more nuanced understanding of economic trends, providing valuable insights that can lead to proactive strategies and improved risk management.

How does the shape (skewness) parameter in the skewed-t model contribute to our understanding of market risks?

The shape parameter, specifically the skewness, within the skewed-t model is crucial for understanding market risks. Skewness reflects the asymmetry of data, indicating potential downside and upside risks. A negative skewness suggests the possibility of significant downside risks, while a positive skewness hints at potential upside opportunities. This parameter allows forecasters to move beyond simple averages and variance to understand the full spectrum of possible outcomes, which is particularly valuable during times of uncertainty. This approach leads to a more informed assessment of market volatility and risk, providing a comprehensive view of potential economic scenarios for better-informed decision-making.