Economic graph overlaid with fog representing increasing uncertainty in future forecasts.

Decoding Economic Forecasts: Are We Overlooking Uncertainty?

Avery Sinclair in Business & Economy December 2025 • 5 min read.

"New methods reveal the hidden risks in GDP predictions, urging a more cautious approach to economic planning and media reporting."

Economic forecasts are a constant presence in our news feeds and policy discussions, particularly those concerning Gross Domestic Product (GDP). Governments, financial institutions, and news outlets routinely publish these predictions, often presented as definitive figures. Take, for instance, a German news website's coverage of GDP forecasts from various institutions. In April 2022, the federal government predicted 2.2% GDP growth for 2022, followed by 2.5% in 2023. Similarly, in July 2022, the European Commission forecasted 1.4% growth for 2022 and 1.3% for 2023. These are examples of 'fixed-event' forecasts, where the event (GDP growth for a specific year) remains constant, but the forecast is updated as time progresses.

The problem with these seemingly straightforward predictions is that they often lack a crucial element: a clear measure of uncertainty. While point forecasts give us a single number, they don't tell us how confident we should be in that number. The degree of uncertainty in a GDP forecast can vary significantly depending on when the forecast is made and the year it pertains to. A July 2022 forecast for 2022 should, intuitively, be more certain than a July 2022 forecast for 2023. But how much more certain? This is difficult to grasp without a quantitative measure of uncertainty.

To address this gap, recent research introduces methods for quantifying the uncertainty inherent in fixed-event point forecasts. By combining point forecasts with historical forecast errors, it’s possible to construct forecast distributions for GDP growth and other key economic indicators. This approach, known as forecast post-processing, is gaining traction across various fields, including meteorology and economics, offering a more practical and insightful alternative to generating forecasts from scratch. Assessing uncertainty based on past errors circumvents the need for deep knowledge of the forecasting process, a significant advantage when dealing with complex or opaque forecasting models.

Unveiling the Fixed-Event Forecasting Framework

Economic graph overlaid with fog representing increasing uncertainty in future forecasts.

Most existing post-processing methods focus on 'fixed-horizon' forecasting, where the time between the forecast and the actual event remains constant. Think of daily temperature forecasts for 12 hours ahead or quarterly inflation rate forecasts. In economics, methods like those developed by Clements (2018) rely on fixed-horizon forecast errors, requiring extensive databases that aren't always available. This is where new tailored tools come into play, designed specifically for the fixed-event case.

The underlying concept is elegantly simple: model quantiles of the forecast error distribution as a function of the forecast horizon. This horizon is defined as the time (in weeks) between the forecast date and the end of the target year. For instance, a forecast made on July 1, 2022, for 2022 has a horizon of 26 weeks, while the same date's forecast for 2023 has a horizon of 78 weeks. Regression models are then used to estimate these relationships, leveraging past forecast errors across different horizons.

Gaussian Heteroscedastic Model: Assumes forecast errors follow a normal distribution with a variance that changes predictably with the forecast horizon.
Decomposition Approach: Separates forecast errors into their sign and magnitude, imposing symmetry assumptions for simplicity.
Flexible Nonparametric Method: Offers a more adaptable approach, estimating forecast error distributions without strict assumptions about their shape.

To illustrate the tangible impact of these methods, consider a hypothetical scenario: an economist, in mid-September, predicts 2.1% GDP growth for the current year and 1.7% for the next. Using these new techniques, a plausible 80% prediction interval for the current year might be 2.1% ± 0.35%, or [1.75%, 2.45%]. However, for the next year, that interval balloons to 1.7% ± 2%, or [-0.3%, 3.7%]. This stark difference underscores the danger of treating current- and next-year forecasts as equally reliable. The next-year forecast, surrounded by significantly greater uncertainty, shouldn't be given the same weight as the current-year prediction.

The Path Forward: Embracing Uncertainty in Economic Discourse

The methods discussed offer a pathway toward more transparent and nuanced economic communication. By acknowledging and quantifying forecast uncertainty, economists and media outlets can provide the public with a more realistic understanding of the economic landscape. This, in turn, can lead to more informed decision-making by individuals, businesses, and policymakers alike. The next step is to integrate these uncertainty measures into routine economic reporting and policy analysis, fostering a more cautious and evidence-based approach to economic planning.

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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.2210.13562,

Title: Prediction Intervals For Economic Fixed-Event Forecasts

Subject: econ.em

Authors: Fabian Krüger, Hendrik Plett

Published: 24-10-2022

Everything You Need To Know

What is the primary problem with current Gross Domestic Product (GDP) forecasts?

The main issue with existing GDP predictions is the lack of a clear measure of uncertainty. While these forecasts, such as those provided by governments and financial institutions, offer a single number, they often fail to indicate the level of confidence associated with that number. This absence of a quantitative measure of uncertainty can mislead decision-makers and the public about the reliability of the forecasts, particularly when comparing predictions for different time horizons.

How do 'fixed-event' forecasts differ from other types of economic predictions?

'Fixed-event' forecasts, as highlighted in the context, involve predictions for a specific event, like the GDP growth for a particular year. The event itself (e.g., GDP growth in 2022 or 2023) remains constant, but the forecast is updated over time. This contrasts with 'fixed-horizon' forecasting, where the time between the forecast and the event is constant, as seen in daily temperature forecasts for the next 12 hours. The methods discussed in the context focus on tailoring tools for fixed-event cases, modeling forecast errors in relation to the time remaining until the end of the target year, or the forecast horizon.

What statistical techniques are used to quantify uncertainty in GDP forecasts and how do they work?

The research introduces innovative methods for quantifying the uncertainty inherent in fixed-event point forecasts. The core approach involves combining point forecasts with historical forecast errors to construct forecast distributions. This is achieved through 'forecast post-processing', which uses the forecast error distribution as a function of the forecast horizon (the time between the forecast date and the end of the target year). This technique allows for methods such as the 'Gaussian Heteroscedastic Model', which assumes forecast errors follow a normal distribution with a variance that changes with the forecast horizon; the 'Decomposition Approach' that separates forecast errors into sign and magnitude; and the 'Flexible Nonparametric Method' that offers a more adaptable approach without strict assumptions about their shape.

Can you explain the implications of using these new methods with an example?

These methods help in illustrating the tangible impact of quantifying uncertainty. For instance, consider an economist predicting 2.1% GDP growth for the current year and 1.7% for the next. Using these new techniques, the plausible 80% prediction interval for the current year might be 2.1% ± 0.35%, or [1.75%, 2.45%]. However, for the next year, that interval can be significantly larger, perhaps 1.7% ± 2%, or [-0.3%, 3.7%]. This dramatic difference reveals that the next-year forecast carries much more uncertainty, and thus should not be given the same weight as the current-year prediction. It underscores the need to consider and communicate the level of uncertainty when interpreting economic forecasts.

How can these techniques improve economic planning and communication?

By acknowledging and quantifying forecast uncertainty, economists and media outlets can offer a more transparent and nuanced understanding of the economic landscape. Integrating these uncertainty measures into routine economic reporting and policy analysis allows for a more evidence-based approach to economic planning. This, in turn, leads to more informed decision-making by individuals, businesses, and policymakers. It encourages a cautious approach to economic planning, making people more aware of the possible variations in economic outcomes and enabling better preparedness for potential risks.