A new working paper from the Reserve Bank of Australia finds that major economic forecasters produce similarly accurate GDP predictions, but share a common flaw: their errors follow predictable patterns that could, in theory, be corrected.
Economists and central bank watchers have long debated whose GDP forecasts are most reliable. New research from the Reserve Bank of Australia suggests the answer may be: no one stands out. Major forecasters — whether government agencies, international bodies, or private institutions — tend to track each other closely in accuracy when predicting economic growth.
But the more striking finding is not the similarity in performance. It is that the errors these forecasters make are not random. When forecasters are wrong, they tend to be wrong in the same direction and at the same moments, suggesting a shared blind spot rather than mere chance.
In statistical terms, a forecast error is called “predictable” when it correlates with information that was already available at the time the forecast was made. If errors could be anticipated, that implies forecasters are systematically overlooking something — perhaps underweighting certain leading indicators, anchoring too strongly to recent trends, or applying models that struggle in turning-point periods like recessions or recoveries.
This type of bias matters beyond academic debate. Central banks, finance ministries, and investors all lean on GDP forecasts to set policy and make decisions. If the errors in those forecasts are systematic, the downstream decisions built on them may carry a hidden tilt that compounds over time.
The RBA paper adds to a growing body of research questioning whether forecast diversity — having many independent voices — actually delivers better information than it appears to. When major forecasters converge on similar numbers and similar errors, the value of any individual forecast may be limited.
For Australia specifically, the findings carry weight as the RBA navigates an uncertain domestic growth outlook. But the paper’s implications reach further: they raise questions about how central banks everywhere use forecasts internally and how much weight policymakers should place on consensus estimates when setting interest rates or fiscal targets.
The key question going forward is whether forecasters can identify and correct these shared biases — or whether the nature of economic uncertainty makes predictable errors an enduring feature of the profession.
