A Fragmented Forecasting Landscape

Economic forecasting is undergoing a structural shift. Models that once converged
around similar projections now yield markedly different outcomes for growth, inflation
and labour dynamics. Divergence is a defining feature of the current macroeconomic
environment.
International institutions acknowledge this challenge. The IMF notes that forecasting
accuracy has weakened due to non-linear shocks and structural breaks that historical
data cannot fully explain. The OECD highlights increasing dispersion across
econometric and machine learning models as uncertainty rises.
The result is a forecasting environment in which traditional reference points no longer
align.

Why Models Diverge

Regime Shifts

Economic relationships that supported forecasting for decades, such as those linking
demand, inflation and employment, behave differently under persistent supply shocks,
demographic change and geopolitical fragmentation.

Data Distortion and Noise

Short term volatility, measurement revisions and inconsistent reporting create noise that
obscures underlying economic signals. Models calibrated on stable periods struggle to
adapt when volatility becomes the norm.

Methodological Gaps

Machine learning models can overfit noise. Structural models rely on assumptions that
may not hold in periods of discontinuity.
So, there is no single model that captures the full spectrum of current risks.

Forecasting Under Divergence

In environments of uncertainty, forecasting becomes an exercise in structured
interpretation rather than prediction.
Effective approaches rely on:
● Multiple model comparison to highlight where assumptions diverge.
● Scenario ranges instead of single point estimates.
● Sensitivity analysis that tests outcomes under alternative regimes.
● Clear assumptions that remain transparent and traceable.
Forecasting becomes less about determining what will happen and more about
understanding how the system behaves under different pressures.

How TAMVER CONSULTING Helps

TAMVER CONSULTING supports clients operating in high uncertainty environments
through:

1.Cross Model Diagnosis: Structured review of model assumptions, divergences
and regime sensitivities.

2.Scenario Architecture: Design of plausible economic futures that integrate
macro, regulatory and geopolitical drivers.

3.Decision Governance: Systems that document reasoning, quantify uncertainty
and ensure forecasts remain defensible.
TAMVER provides clarity and structure when model divergence makes economic
interpretation more complex.

References
● IMF: World Economic Outlook, April 2024: forecasting uncertainty under
structural breaks.
● OECD: Economic Outlook, 2024: increased dispersion in model predictions.