Predictive Synthesis under Sporadic Participation: Evidence from Inflation Density Surveys

TL;DR

This paper introduces a Bayesian method for combining inflation forecasts from sporadically participating forecasters, improving accuracy and interpretability over traditional aggregation methods.

Contribution

It develops a Bayesian updating framework that accounts for irregular participation, maintaining latent forecaster states without imputation or renormalization.

Findings

Enhanced predictive accuracy over benchmarks

Smoother, better-calibrated inflation density forecasts

Effective during periods of high forecaster turnover

Abstract

Central banks rely on density forecasts from professional surveys to assess inflation risks and communicate uncertainty. A central challenge in using these surveys is irregular participation: forecasters enter and exit, skip rounds, and reappear after long gaps. In the European Central Bank's Survey of Professional Forecasters, turnover and missingness vary substantially over time, causing the set of submitted predictions to change from quarter to quarter. Standard aggregation rules -- such as equal-weight pooling, renormalization after dropping missing forecasters, or ad hoc imputation -- can generate artificial jumps in combined predictions driven by panel composition rather than economic information, complicating real-time interpretation and obscuring forecaster performance. We develop coherent Bayesian updating rules for forecast combination under sporadic participation that maintain a well-defined latent predictive state for each forecaster even when their forecast is unobserved. Rather than relying on renormalization or imputation, the combined predictive distribution is updated through the implied conditional structure of the panel. This approach isolates genuine performance differences from mechanical participation effects and yields interpretable dynamics in forecaster influence. In the ECB survey, it improves predictive accuracy relative to equal-weight benchmarks and delivers smoother and better-calibrated inflation density forecasts, particularly during periods of high turnover.