Forecast errors shed light on economic shock types
Economic forecasts often miss the mark, but the patterns of these errors provide valuable insights. This ECB blog post explains how analyzing forecast errors can reveal the nature of underlying economic shocks, particularly during uncertain periods.
Unpacking demand and supply surprises
Forecast errors provide a valuable window into underlying economic forces.
If GDP and inflation errors move in the same direction, the surprise is typically demand-driven, reflecting unexpected spending or policy effects.
Conversely, if errors move in opposite directions, supply-side shocks like energy price swings or supply chain disruptions are more likely.
This mapping helps interpret forecast misses without complex modeling.
Over the past two decades, supply-driven surprises have accounted for the largest share of forecast errors, particularly in one-year-ahead projections.
Since the pandemic, negative supply shocks have weighed on growth, while resilient demand partially offset this impact.
Recent observations indicate a continued mix of mild demand and supply surprises.
Systematic misses and structural drivers
A small model breaks down forecast error drivers into demand, supply, and energy shocks.
Systematic forecast misses were primarily driven by underestimated supply-side shocks, particularly during volatile periods.
Energy prices, global supply disruptions, and other external disturbances accounted for a substantial share of both GDP and inflation forecast errors.
This highlights that large, predominantly external shocks were the dominant source of forecast misses in the past.
More recently, errors have narrowed, with contributions more evenly distributed, reflecting less extreme shocks and improved forecasting.
Errors as a policy compass
Learning from forecast errors is essential for policymakers facing frequent macroeconomic shocks.
This approach sharpens baseline narratives, helps design realistic risk scenarios, and enhances communication on uncertainty.
Ultimately, turning errors into an analytical tool refines understanding of the economy and improves anticipation of future risks.