Forecasting models fail in structural uncertainty
Multiple scenarios replace single baseline thinking
Arab banks must adapt to shifting realities
The global economic landscape has rarely appeared as unsettled as it does today. Recent geopolitical developments, particularly the escalation of conflict in the Middle East, have once again exposed the fragility of long-standing economic assumptions. Energy markets have reacted sharply, supply routes have come under strain, and policymakers across the world find themselves confronting a familiar yet unresolved question: how to anticipate what lies ahead when the foundations of the system itself appear to be shifting.
For decades, economic forecasting rested on a relatively stable premise. From the mid-1980s until the onset of the COVID-19 pandemic, the global economy exhibited a degree of predictability that allowed central banks, ministries of finance, and financial institutions to operate with a high level of confidence. Inflation remained contained, supply chains functioned efficiently, and the transmission mechanisms of economic shocks were broadly understood. Within such an environment, forecasting tools evolved accordingly—favoring a central baseline projection supplemented by probabilistic ranges that reflected potential deviations.
This framework was not without merit. By anchoring expectations around a single, coherent outlook, policymakers were able to communicate clearly with markets and the public. The use of fan charts—illustrating a range of possible outcomes around the baseline—offered a structured way to express uncertainty while preserving analytical discipline. Implicit in this approach, however, was a critical assumption: that the future would, in essence, resemble the past.
That assumption has come under increasing strain.
The pandemic marked a decisive break from historical patterns. Supply chains, once considered resilient and efficient, proved vulnerable to disruptions that propagated rapidly across borders and sectors. Inflation dynamics, long subdued, re-emerged with unexpected force. Central banks, despite sophisticated models and extensive data, found themselves consistently underestimating the scale and persistence of price pressures. What had been treated as temporary distortions revealed deeper structural shifts within the global economy.
The current geopolitical environment reinforces this reality. The conflict affecting key energy corridors has introduced a new layer of complexity, particularly for economies that remain heavily dependent on imported energy. While historical precedents—such as past oil crises—offer some reference points, the present context differs in significant ways. The interplay of regional actors, the configuration of global energy markets, and the broader economic backdrop combine to create a situation without a direct parallel.
In such circumstances, the distinction between risk and uncertainty becomes more than a theoretical nuance. The economist Frank Knight articulated this difference over a century ago. Risk, in his framework, refers to situations where outcomes are uncertain but quantifiable; probabilities can be assigned based on historical experience. Uncertainty, by contrast, arises when the underlying structure itself is evolving in ways that defy prior observation. In these cases, probabilities lose their meaning because the range of possible outcomes cannot be reliably defined.
Today’s economic environment bears the hallmarks of what is often described as “Knightian uncertainty.” The challenge is not merely that outcomes are difficult to predict, but that the mechanisms driving those outcomes are themselves in flux. Under such conditions, traditional forecasting tools—particularly those that rely on historical relationships—risk providing a false sense of precision.
The limitations of conventional approaches are most evident in the reliance on single baseline forecasts. While useful in stable environments, a single projection struggles to capture fundamentally different trajectories that may arise from structural changes. Expanding the range of a fan chart does little to address this issue. A wider band may signal greater uncertainty, but it still assumes that the future can be described as a variation of the past. When the “story” of the economy is itself uncertain, such an approach becomes insufficient.
An alternative framework has begun to gain traction among leading central banks and economic institutions. Rather than presenting a single forecast, this approach emphasizes the development of multiple, well-defined scenarios. Each scenario represents a distinct and internally coherent narrative about how economic conditions may evolve, supported by corresponding projections for key variables such as growth, inflation, and interest rates.
The strength of this method lies in its ability to accommodate fundamentally different outcomes. For instance, in the context of current energy market disruptions, one scenario may assume that tensions remain contained, allowing supply chains to stabilize and prices to gradually revert toward previous levels. Another scenario may consider the possibility of escalation, leading to sustained supply constraints, elevated energy prices, and broader second-round effects on inflation and economic activity. A third scenario might explore intermediate dynamics, reflecting partial disruptions and uneven adjustments across regions.
Importantly, scenario-based forecasting is not merely an exercise in speculation. Each scenario is accompanied by a clear narrative explaining the economic logic at play, as well as specific indicators that would signal a shift from one trajectory to another. This structured approach enhances transparency and provides decision-makers with a more nuanced understanding of potential risks and opportunities.
Several major central banks have already begun to adopt this framework. The European Central Bank, for example, has moved away from traditional fan charts in favor of presenting multiple scenarios, explicitly acknowledging the limitations of probabilistic tools under conditions of elevated uncertainty. Similarly, Sweden’s Riksbank has incorporated alternative scenarios into its communications, while the Bank of Canada has, at times, refrained from issuing a single baseline forecast when no credible projection could be established.
These developments reflect a broader shift in how institutions approach uncertainty. Rather than striving for precision in an inherently unpredictable environment, the emphasis is increasingly placed on clarity, flexibility, and intellectual honesty. Credibility, in this context, is not derived from consistently accurate predictions—an unrealistic expectation in any case—but from the ability to articulate the range of plausible outcomes and to adjust assessments as new information emerges.
For the Arab region, the implications are particularly significant. Many economies across the Middle East and North Africa are deeply integrated into global energy markets, either as exporters or importers. Fluctuations in energy prices have direct consequences for fiscal balances, inflation, and external accounts. At the same time, ongoing efforts to diversify economic structures and enhance resilience add another layer of complexity to the policy environment.
In such a context, the adoption of more sophisticated forecasting frameworks is not merely an academic exercise; it is a strategic necessity. Financial institutions, in particular, must operate with a clear understanding of how different scenarios may affect asset quality, liquidity conditions, and capital adequacy. Policymakers, meanwhile, require tools that support informed decision-making without overstating the degree of certainty.
The shift toward scenario-based analysis also has implications for communication. In a region characterized by diverse economic structures and varying degrees of exposure to external shocks, clear and transparent communication becomes essential. By presenting multiple scenarios, institutions can convey the inherent uncertainty of the environment while providing stakeholders with actionable insights. This approach fosters a more informed dialogue between policymakers, markets, and the public.
Moreover, scenario-based forecasting aligns closely with broader trends in risk management. Stress testing, for example, already relies on the evaluation of extreme but plausible scenarios to assess the resilience of financial institutions. Integrating such thinking into macroeconomic forecasting creates a more coherent analytical framework, bridging the gap between high-level projections and institution-specific risk assessments.
Looking ahead, the challenge will be to refine these approaches and embed them within institutional processes. Designing meaningful scenarios requires a combination of analytical rigor and practical judgment. It involves not only the identification of key uncertainties but also an understanding of how these uncertainties interact across sectors and regions. This, in turn, calls for enhanced collaboration between economists, market analysts, and policymakers.
For Arab banks and financial leaders, several practical considerations emerge. First, there is a need to move beyond reliance on single-point forecasts in strategic planning. Incorporating multiple scenarios into decision-making processes can improve resilience and enable institutions to respond more effectively to changing conditions. Second, investment in analytical capabilities—particularly in areas such as data analysis and economic modeling—will be essential to support more sophisticated forecasting frameworks. Third, communication strategies should be adapted to reflect the complexity of the environment, balancing clarity with an honest acknowledgment of uncertainty.
In conclusion, the current phase of global economic uncertainty underscores the limitations of traditional forecasting methods and highlights the value of more flexible, scenario-based approaches. For the Arab banking sector, embracing this shift offers an opportunity to enhance resilience, strengthen decision-making, and reinforce credibility in an increasingly complex environment. By adopting frameworks that reflect the realities of structural change, institutions can better position themselves to respond to uncertainty—not with unwarranted confidence, but with informed and disciplined judgment.
