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.

翻译：中央银行依赖专业调查的密度预测来评估通胀风险并传达不确定性。使用这些调查的一个核心挑战是不规则参与：预测者进入和退出、跳过调查轮次、并在长期间隔后重新出现。在欧洲中央银行的专业预测者调查中，人员更替和缺失情况随时间显著变化，导致提交的预测集合每季度都在改变。标准聚合规则——例如等权重汇集、剔除缺失预测者后重新归一化或临时插补——可能因调查面板构成（而非经济信息）导致组合预测出现人为跳跃，使实时解读复杂化并模糊预测者表现。我们针对稀疏参与下的预测组合开发了连贯的贝叶斯更新规则，即使预测者的预测未被观测到，也能为其保持定义良好的潜在预测状态。该方法不依赖重新归一化或插补，而是通过面板的隐含条件结构更新组合预测分布。此方法将真实的性能差异与机械性参与效应分离，并产生可解释的预测者影响力动态。在欧央行调查中，相较于等权重基准，该方法提高了预测精度，并提供了更平滑且校准更优的通胀密度预测，尤其在人员更替频繁时期表现突出。