Economy, explained.
IMF headquarters in Washington DC on 13 April 2026 (Aaron Schwartz/Bloomberg via Getty Images)
Predictions are losing authority and the institutions that produce them are only beginning to adapt.
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Economic forecasts have never been more prominent – or less reliableEconomic forecasts have never been more prominent – or less reliable
The release of flagship projections such as the International Monetary Fund’s World Economic Outlook continues to shape the global economic calendar. Governments recalibrate expectations, markets react, and analysts parse revisions for signals about the future.
Yet the widening gap between forecasts and outcomes raises a fundamental question: what role does economic prediction play in a world where uncertainty is not episodic, but structural?
Demand for forecasts has never been higher. Policymakers, investors, and business yearn for anchors in an uncertain world. Most still require a baseline to anchor decisions from annual budgets to long-term capital allocation, making forecasts an essential organising device. But their authority is eroding because as the range of plausible outcomes expands, the signal loses meaning.
This is not simply a story of bad forecasting, but reflects fundamental changes in the global system.
Policy is no longer a stable input into models; it is itself a dynamic variable.
Modern economic forecasting emerged alongside national income accounting in the mid-20th century. From the late 1940s, governments in the United States and United Kingdom began producing systematic projections of growth, inflation and employment. By the 1960s and 1970s, forecasting had become institutionalised across official and private sectors. These forecasts were built on a relatively stable set of assumptions: fiscal responsibility and central banks focused on macroeconomic management. Within these guard rails, models could generate a bounded set of plausible outcomes.
Those guard rails are weakening. Fiscal policy is deployed as an instrument of strategic competition along with tolerance of mounting public debt. Central banks, while still formally independent, are navigating complex and politically charged mandates. Trade policy is shaped by geopolitical considerations rather than global rules. Policy is no longer a stable input into models; it is itself a dynamic variable.
At the same time, structural transformations are undermining the empirical relationships on which forecasts depend. Demographic change is altering labour supply and consumption patterns. Climate transition is reshaping production and investment decisions. Technological advances are affecting productivity dynamics and market structure in ways that are little understood, let alone modelled.
Technology does not, at least so far, resolve the underlying challenge. The difficulty is not simply in data or computation, but in modelling a system whose structure is itself evolving. Large language models and big data expand the informational frontier, but they do not yet resolve the core problem of system instability.
Policy decisions require a focal point, and a central projection provides a common reference for markets and the public (Markus Spiske/Unsplash)
Taken together, these developments point to a deeper shift in the nature of economic prediction. One way to understand this shift is through the emergence of three broad approaches.
The traditional model treats prediction as estimation. It assumes that stable statistical relationships can be identified in historical data and projected forward. This approach underpinned the post-war forecasting industry and remains embedded in most institutional practice.
A second approach treats prediction as simulation. Here, the economy is understood as a complex, adaptive system in which outcomes emerge from the interaction of agents, policies and shocks. Forecasts become conditional statements: if certain policies or conditions hold, particular outcomes are likely to follow.
A third approach recognises the limits of both estimation and simulation under conditions of deep uncertainty. In this view, probabilities themselves may be unknowable. Scenario-based analysis replaces point forecasts, focusing on mapping plausible futures rather than identifying a single expected outcome.
In practice, all three approaches now coexist. Central banks provide a useful lens on how institutions are adapting.
At the level of public communication, little has changed. Monetary policy statements continue to revolve around a baseline forecast for inflation and growth. This reflects both institutional legacy and practical necessity. Policy decisions require a focal point, and a central projection provides a common reference for markets and the public.
Beneath this surface, however, forecasting practice has become nuanced. Traditional inflation targeting central banks use fan charts to show probability distributions around their forecasts. But these charts are based on increasingly irrelevant historical relationships. More recently, the Reserve Bank of Australia has joined the European Central Bank and others in presenting alternative scenarios around energy prices and geopolitical risks to illustrate the impact of different shocks. The Reserve Bank of New Zealand has pioneered the use of conditional policy paths within its projections.
Greater precision will give way to greater relevance.
These developments signal a shift away from reliance on a single expected outcome toward an assessment of possible futures. Yet the most sophisticated elements of this analysis are typically located in supporting documents rather than in the core policy statement. Public communication remains anchored in a baseline forecast, supplemented by qualitative discussion of risks. This separation highlights a central tension. The internal reality of economic analysis is increasingly complex and system-based, while external communication remains necessarily simplified.
For the economics prediction industry, the implications are far-reaching.
First, point forecasts are likely to play a more limited role. They will remain necessary as reference points, but rather than being treated as precise predictions, they will be understood as conditional and contingent. Second, scenario analysis will become more central. The key question shifts from “what will happen?” to “what could happen under different conditions?”. This reframes forecasting as an exercise in exploring uncertainty rather than resolving it.
The third shift – where the greatest potential lies – is interpretation. In a world of structural change, developing the ability to identify key drivers, articulate insightful narratives, and bring clarity to the conditions under which different outcomes emerge becomes the substantive focus rather than just producing a single number.
The economics prediction industry is therefore not disappearing, but it is certainly being reshaped. Greater precision will give way to greater relevance – helping decision-makers manage complexity, weigh risk, and adapt to a global system in which the future cannot be known with confidence.
About the author
David Nellor
David Nellor is a Senior Fellow at the Lowy Institute. He is a PhD economist with extensive international experience across economic policy, development, financial markets, and the climate and energy transition. His work combines applied economic expertise with advanced data analytics to support policy design, market analysis, and strategic decision-making.
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