STEP 01
Match the comparison
Use the same country, indicator, target year, definitions and survey date for the house and consensus forecasts.
A CONSENSUS USE CASE
Your forecast helps shape the consensus. The consensus gives it context.
Professional private sector forecasters contribute independent economic views to a collective benchmark. For participating economists, the published consensus offers a useful reference for examining where their own forecasts differ, how the panel is changing and which assumptions merit another look.
THE QUESTION
“Where does our forecast stand within the wider panel—and why?”
Contributing ForecastersCONSENSUS ECONOMICS
01 / THE SITUATION
Here’s the decision.
The survey review
A forecasting team reviews its inflation outlook after a new survey round.
The team expects inflation to stay above the panel mean. That difference may reflect a genuinely different view of wage dynamics, energy costs or monetary policy. The economists want to assess how unusual their forecast is, whether the gap has changed since the prior survey and whether other contributors share the concern.
THE OBJECTIVE
Document the house view against the independent panel benchmark without adjusting it merely to follow the crowd.
02 / THE WORKFLOWFrom raw question
From raw question
to useful evidence.
Compare your house view with the panel on matched survey terms, investigate the direction and breadth of revisions, then record the assumptions supporting any divergence.
STEP 02
Examine the distribution
Locate the house estimate against the mean, high and low forecasts, standard deviation and available individual panel views.
STEP 03
Track relative revisions
Compare how the house view and the panel changed between survey rounds; investigate whether a wider gap reflects fresh information or different assumptions.
STEP 04
Record the rationale
Explain which data and judgements justify the divergence and revisit accuracy when the target period resolves.
03 / A PRACTICAL EXAMPLEA worked example.
A worked example.
The numbers in context.
Is our house inflation view genuinely outside the panel?
After a monthly survey, a contributing economist compares the team’s forecast for the same country and target year with two successive panel results.
WORKING EXAMPLEIllustrative example
Swipe sideways to view the complete example table
| Matched inflation forecast | Earlier survey | Latest survey |
|---|---|---|
| House forecast | 3.07% | 3.24% |
| Consensus mean | 2.72% | 2.87% |
| House minus consensus | +0.35 pp | +0.37 pp |
THE PRACTICAL DELIVERABLE
A short benchmark note: house forecast, panel mean and range, revision gap, distinctive assumptions and questions for the next survey.
SUITABLE STARTING POINTUnderstand our survey methodology
See how individual contributions form the mean and distribution. Availability of panel detail follows the applicable participation arrangements.
GO FURTHER / APPLY THE FULL DISTRIBUTION
The range behind
The range behind
the average.
Use the consensus as a diagnostic benchmark for a house forecast—not a target to imitate. The distribution reveals whether a distinctive view is widely shared and whether forecasters have revised together.
FORECAST DISTRIBUTIONInflation forecast for a matched country and target year
Consensus mean2.87%
Low2.11%
High3.68%
Standard deviation0.39 pp
BEHIND THE AVERAGE
Selected individual panel estimates: 2.52% and 3.31%. A house forecast of 3.24% sits above the mean, but inside the submitted high–low range.
LOW · 2.11%HIGH · 3.68%
Consensus mean Selected individual forecasts
A PRACTICAL SCENARIO OR RESEARCH TEST
Illustrative exampleChallenge the house inflation view without following the crowd
Swipe sideways to review the scenario table
| Benchmark lens | Inflation input | Question for the forecasting team |
|---|---|---|
| House forecast | 3.24% | Which wage, demand or policy assumptions support a view 0.37 pp above the mean? |
| Panel mean | 2.87% | Has the average changed because many forecasters revised or a few moved sharply? |
| Highest submitted view | 3.68% | Do the reasons behind the upper-end forecasts overlap with the house thesis? |
The high and low describe submitted forecasts, not confidence limits. Standard deviation measures cross-forecaster disagreement. Neither requires the house view to converge towards the mean.
OTHER APPLICATIONS FOR THIS AUDIENCE
Further applications.
Pre-survey assumptions review
Check whether a macroeconomic forecast set remains internally coherent before submission.
USE THE DATA
Compare house GDP, CPI and rate assumptions with matched panel series and revisit forecast definitions and underlying drivers.
OUTPUT
A survey submission checklist and an assumptions log.
Long-run forecast calibration
Evaluate when the team was distinctively right or wrong over successive survey cycles.
USE THE DATA
Track dated house and consensus vintages, realised outcomes and forecast errors for comparable target periods.
OUTPUT
A forecast-performance and revisions review that informs—not dictates—future judgement.
04 / WHAT YOU TAKE AWAYA practical result.
A practical result.
Ready to review.
Build an output that can be explained, challenged and updated as forecasts change.
How the consensus is calculatedEXAMPLE DELIVERABLE↗
Illustrative forecaster deliverableA house-view benchmark and assumptions note
A concise comparison of the team’s estimate against the panel distribution, with the key drivers of any gap and a record of changes since the previous survey.
WHAT IT CONTAINS
Adapt the example to your own data and decision process.✓House forecast versus panel mean and range
✓Gap and revision history for matched horizons
✓Assumptions, evidence and reasons to retain or revise the house view
05 / THE CONSENSUS ADVANTAGEWhy the data
Why the data
matter here.
Match the forecast inputs to the decision. The average, the distribution and the historical record serve different purposes.
RELEVANT FORECAST INPUTS
01
Consensus mean, high and low forecasts
02
Individual contributor views and standard deviation where supplied
03
Survey-to-survey revisions and consistent historical records
WHY CONSENSUS ECONOMICS
A benchmark built from the forecasters themselves.
Each contribution helps construct the professional consensus. The mean locates the wider view; high and low estimates, individual forecasts and standard deviation show how much company the house view has. Comparing vintages helps separate fresh conviction from panel-wide change, without treating agreement as a goal.
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