STEP 01
Frame the hypothesis
Specify countries, dates, forecast horizons and measures of disagreement.
A CONSENSUS USE CASE
Research the forecast—and the thinking behind it.
A single average cannot explain how expectations are formed. The individual forecasts of professional private sector forecasters, survey records and historical continuity behind Consensus Economics can support rigorous research into how expectations evolve.
THE QUESTION
“When do experts disagree—and what happens to that disagreement over time?”
Academics & ResearchersCONSENSUS ECONOMICS
01 / THE SITUATION
Here’s the decision.
The research question
An economics researcher is studying forecast disagreement after an inflation shock.
The paper asks whether inflation uncertainty is reflected in a wider spread of professional expectations, and how quickly that dispersion narrows. Comparing a headline average alone will not answer the question.
THE OBJECTIVE
Create a transparent historical panel of professional expectations, then test changes in level, range and dispersion.
02 / THE WORKFLOWFrom raw question
From raw question
to useful evidence.
Hold forecast horizons and definitions constant to test how consensus revisions, forecaster disagreement and accuracy evolve.
STEP 02
Build the historical panel
Combine relevant survey vintages and individual forecast observations where the dataset includes them.
STEP 03
Measure changes in beliefs
Study consensus revisions, high–low ranges and standard deviation alongside relevant external events.
STEP 04
Document the methodology
Record coverage changes, survey frequency, missing observations and comparability decisions.
03 / A PRACTICAL EXAMPLEA worked example.
A worked example.
The numbers in context.
Did forecasters disagree more during an inflation shock?
A researcher assembles three comparable survey rounds for one inflation forecast horizon, then measures both the consensus level and the spread of individual forecasts.
WORKING EXAMPLEIllustrative example
Swipe sideways to view the complete example table
| Comparable survey period | Mean inflation expectation | Cross-forecaster SD |
|---|---|---|
| Before the shock | 2.34% | 0.31 percentage points |
| During the shock | 3.16% | 0.83 percentage points |
| Subsequent period | 2.94% | 0.51 percentage points |
THE PRACTICAL DELIVERABLE
Replicable country-by-survey panel, disagreement charts and a methods appendix identifying sample changes.
SUITABLE STARTING POINTExplore the historical databank
Historical survey vintages with consensus and panel-level detail for covered country and indicator series.
GO FURTHER / APPLY THE FULL DISTRIBUTION
The range behind
The range behind
the average.
Study disagreement, learning and forecasting performance using repeated professional survey vintages. Keep the composition of the panel visible.
FORECAST DISTRIBUTIONInflation forecast, same target period following the shock
Consensus mean2.94%
Low1.89%
High4.09%
Standard deviation0.51 pp
BEHIND THE AVERAGE
Example individual forecasts: 2.41% and 3.35%. The corresponding cross-forecaster variance is about 0.26 pp² (standard deviation squared). Individual views also support tests of forecast accuracy and learning.
LOW · 1.89%HIGH · 4.09%
Consensus mean Selected individual forecasts
A PRACTICAL SCENARIO OR RESEARCH TEST
Illustrative exampleTest whether disagreement survives robustness checks
Swipe sideways to review the scenario table
| Research specification | Measured SD | Interpretation to test |
|---|---|---|
| Full panel after shock | 0.51 pp | Reference disagreement across the surveyed panel. |
| Matched contributors only | 0.49 pp | Does the rise persist after excluding panel turnover? |
| Common-horizon sample | 0.44 pp | Is dispersion driven by changing forecast horizons? |
Cross-sectional standard deviation measures disagreement between point forecasts; it is not a direct probability distribution of future inflation or the uncertainty held by each forecaster.
OTHER APPLICATIONS FOR THIS AUDIENCE
Further applications.
Forecast efficiency and bias
Investigate systematic over- or underprediction across economies.
USE THE DATA
Compare contemporaneous vintages with outcomes and inspect error patterns by individual forecaster and horizon.
OUTPUT
A reproducible forecast-error panel and robustness appendix.
Heterogeneity through shocks
Ask who adjusts fastest when economic conditions change.
USE THE DATA
Compare matched forecaster revisions, high-low spread and standard deviation before and after an event.
OUTPUT
An academic panel study and visualisation of dispersion dynamics.
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 academic deliverableA replicable expectations study
A documented dataset, clear variable definitions and reproducible exhibits showing how professional expectations changed.
WHAT IT CONTAINS
Adapt the example to your own data and decision process.✓Country-by-survey expectations panel
✓Disagreement and revision plots
✓Methods appendix and coverage caveats
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
Historical survey vintages since available start dates
02
Individual forecasts and distribution statistics
03
Long-term and quarterly forecasts where relevant
WHY CONSENSUS ECONOMICS
The observations behind the average.
Individual forecasts permit analysis of disagreement and contributor-level behaviour. Standard deviation and high–low ranges help describe cross-sectional spread, while repeated surveys make changes in expectations observable over time.
1,000+professional forecasters1989first survey