Matthew C. Johnson, Matteo Luciani, Minzhengxiong Zhang, Kenichiro McAlinn
arXiv 5 Feb 2026 · Statistics — Applications
arXiv:2602.05226 · PDF · DOI · OpenAlex · Extracted main text
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.
appendix boundary found by none_found · 100% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | McAlinn, Kenichiro and West, Mike (2019) Dynamic Bayesian predictive synthesis in time series forecasting self | 1.000 | 5 | 3 | 100% |
| 2 | V. Genre and G. Kenny and A. Meyler and A. Timmermann (2013) Combining expert forecasts: Can anything beat the simple average? | 0.644 | 2 | 2 | 100% |
| 3 | McAlinn, Kenichiro (2021) Mixed-frequency Bayesian predictive synthesis for economic nowcasting self | 0.644 | 2 | 2 | 100% |
| 4 | J. F. Geweke and G. G. Amisano (2011) Optimal prediction pools | 0.511 | 2 | 1 | 100% |
| 5 | K. A. Aastveit and K. R. Gerdrup and A. S. Jore and L. A. Thorsrud (2014) Nowcasting GDP in real time: A density combination approach | 0.405 | 1 | 1 | 100% |
| 6 | G. G. Amisano and R. Giacomini (2007) Comparing density forecasts via weighted likelihood ratio tests | 0.405 | 1 | 1 | 100% |
| 7 | J. M. Bates and C. W. J. Granger (1969) The combination of forecasts | 0.405 | 1 | 1 | 100% |
| 8 | M. Billio and R. Casarin and F. Ravazzolo and H. K. van Dijk (2012) Combination schemes for turning point predictions | 0.405 | 1 | 1 | 100% |
| 9 | Chernis, Tony and Koop, Gary and Tallman, Emily and West, Mike (2024) Decision synthesis in monetary policy | 0.405 | 1 | 1 | 100% |
| 10 | R. T. Clemen (1989) Combining forecasts: A review and annotated bibliography | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 28 scored citations.