STEP 01
Select the policy indicators
Specify relevant price, activity, rate or currency measures and matching horizons.
A CONSENSUS USE CASE
See how expectations move around policy.
Public bodies, central banks and policy analysts frequently need to distinguish their own projections from external expectations. A long-running survey of professional private sector forecasters provides another lens through which to interpret the outlook.
THE QUESTION
“How are professional private sector forecasters responding to changes in the policy outlook?”
Policy & InstitutionsCONSENSUS ECONOMICS
01 / THE SITUATION
Here’s the decision.
The policy briefing
An economic analysis team is preparing a briefing ahead of a policy review.
Recent data have changed the short-term outlook, but the team wants to know whether private sector expectations for growth and inflation have moved with them. It also needs to know if disagreement is increasing.
THE OBJECTIVE
Bring a transparent outside-expectations section to the briefing, with a clear description of uncertainty.
02 / THE WORKFLOWFrom raw question
From raw question
to useful evidence.
Compare like-for-like expectations with institutional projections, then explain where consensus revisions and disagreement alter the policy briefing.
STEP 02
Compare expectations
Set the professional consensus beside the institution’s own projections without treating either as definitive.
STEP 03
Track survey-to-survey changes
Distinguish shifts in the consensus from changes in the distribution of individual forecasts.
STEP 04
Explain the limitations
Document survey dates, coverage and the difference between an expectation and an outcome.
03 / A PRACTICAL EXAMPLEA worked example.
A worked example.
The numbers in context.
Is the outside inflation view moving away from the official projection?
A policy team prepares an inflation briefing. It compares the published institutional projection with successive professional survey releases for the same country, indicator and target period.
WORKING EXAMPLEIllustrative example
Swipe sideways to view the complete example table
| Comparable measure | Earlier survey | Later survey |
|---|---|---|
| Professional mean inflation forecast | 2.43% | 2.76% |
| Individual forecast range | 1.84%–3.06% | 1.93%–3.67% |
| Institutional projection | 2.51% | 2.51% |
THE PRACTICAL DELIVERABLE
Outside-expectations slide: current consensus versus official projection, prior-survey comparison, dispersion and dates.
SUITABLE STARTING POINTExplore historical Excel data
Monthly survey history for monitoring revisions and, where included, the distribution of forecaster views.
GO FURTHER / APPLY THE FULL DISTRIBUTION
The range behind
The range behind
the average.
Use the panel distribution to challenge an official baseline, track disagreement and prepare a balanced policy briefing. These are sensitivity inputs, not probability-weighted forecasts.
FORECAST DISTRIBUTIONConsumer-price inflation, matching country and target year
Consensus mean2.76%
Low1.93%
High3.67%
Standard deviation0.44 pp
BEHIND THE AVERAGE
Selected individual forecaster views: 2.14% and 3.29%. Review the underlying panel to see whether revisions are broad-based or driven by a few contributors.
LOW · 1.93%HIGH · 3.67%
Consensus mean Selected individual forecasts
A PRACTICAL SCENARIO OR RESEARCH TEST
Illustrative exampleChallenge the policy baseline with the distribution
Swipe sideways to review the scenario table
| Policy briefing lens | Inflation input | Question for the team |
|---|---|---|
| External reference | 2.76% | How far has the survey mean moved from the 2.51% official projection? |
| Persistent inflation sensitivity | 3.67% | What would a high-inflation input change in the policy narrative? |
| Faster disinflation sensitivity | 1.93% | Which assumptions would be challenged if inflation fell faster? |
The high and low are submitted individual forecasts, not confidence bounds. A multi-variable policy scenario must be constructed separately, with internally consistent growth, rates and inflation paths.
OTHER APPLICATIONS FOR THIS AUDIENCE
Further applications.
Policy credibility monitor
Track revisions after major policy announcements.
USE THE DATA
Compare matched survey vintages and changes in standard deviation, alongside official projections.
OUTPUT
An expectations dashboard with a timeline and disagreement flags.
Financial stability scenario challenge
Test whether institution-designed stress assumptions sit far outside the professional distribution.
USE THE DATA
Use the mean as an external anchor; compare selected individual forecasts and then design a severe but plausible stress case.
OUTPUT
A documented baseline-versus-stress assumptions sheet.
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 policy deliverableAn external expectations briefing
A concise explanation of what professional forecasters expect, where the range of opinion lies and how it has changed.
WHAT IT CONTAINS
Adapt the example to your own data and decision process.✓Official projection versus consensus
✓Revision and dispersion context
✓Survey-methodology footnotes
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
Monthly professional macroeconomic surveys
02
Historic revisions and forecast distributions
03
Additional long-horizon series where covered
WHY CONSENSUS ECONOMICS
A comparable outside view for policy analysis.
The mean gives a reference point; individual forecasts, high and low estimates, and standard deviation show whether a policy-relevant change is widely shared or contested. Preserved survey vintages reveal when the shift occurred.
1,000+professional forecasters1989first survey