Francis X. Diebold, Aaron Mora, Minchul Shin
arXiv 12 Mar 2025 · Econometrics
arXiv:2503.09287 · PDF · DOI · OpenAlex · Extracted main text
We study the properties of macroeconomic survey forecast response averages as the number of survey respondents grows. Such averages are "portfolios" of forecasts. We characterize the speed and pattern of the gains from diversification and their eventual decrease with portfolio size (the number of survey respondents) in both (1) the key real-world data-based environment of the U.S. Survey of Professional Forecasters (SPF), and (2) the theoretical model-based environment of equicorrelated forecast errors. We proceed by proposing and comparing various direct and model-based "crowd size signature plots," which summarize the forecasting performance of k-average forecasts as a function of k, where k is the number of forecasts in the average. We then estimate the equicorrelation model for growth and inflation forecast errors by choosing model parameters to minimize the divergence between direct and model-based signature plots. The results indicate near-perfect equicorrelation model fit for both growth and inflation, which we explicate by showing analytically that, under conditions, the direct and fitted equicorrelation model-based signature plots are identical at a particular model parameter configuration, which we characterize. We find that the gains from diversification are greater for inflation forecasts than for growth forecasts, but that both gains nevertheless decrease quite quickly, so that fewer SPF respondents than currently used may be adequate.
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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 | Bates and Granger (1969) The Combination of Forecasts, Operations Research Quarterly\/, 20, 451–468 | 0.843 | 4 | 3 | 75% |
| 2 | Engle and Kelly (2012) Dynamic Equicorrelation, Journal of Business and Economic Statistics\/, 30, 212–228 | 0.811 | 4 | 2 | 100% |
| 3 | Elliott and Liao (2025) Combining Forecasts – On Why Averaging Beats Optimal Linear Weights, Working paper, University of California, San Diego | 0.737 | 3 | 2 | 100% |
| 4 | Croushore and Stark (2019) Fifty Years of the Survey of Professional Forecasters, Economic Insights\/, 4, 1–11 | 0.644 | 2 | 2 | 100% |
| 5 | Clemen (1989) Combining Forecasts: A Review and Annotated Bibliography (With Discussion), International Journal of Forecasting\/, 5, 559–583 | 0.644 | 2 | 2 | 100% |
| 6 | Genre, Kenny, Meyler, and Timmermann (2013) Combining Expert Forecasts: Can Anything Beat the Simple Average? International Journal of Forecasting\/, 29, 108–121 | 0.644 | 2 | 2 | 100% |
| 7 | Makridakis and Winkler (1983) Averages of Forecasts: Some Empirical Results, Management Science\/, 29, 987–996 | 0.511 | 2 | 1 | 100% |
| 8 | Arifovic, Bullard, and Kostyshyna (2013) Social Learning and Monetary Policy Rules, The Economic Journal\/, 123, 38–76 | 0.405 | 1 | 1 | 100% |
| 9 | Batchelor and Dua (1995) Forecaster Diversity and the Benefits of Combining Forecasts, Management Science\/, 41, 68–75 | 0.405 | 1 | 1 | 100% |
| 10 | Chauvet and Potter (2013) Forecasting Output, Handbook of Economic Forecasting\/, 2, 141–194 | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 31 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | A Kernel Score Perspective on Forecast Disagreement and the Linear Pool | 0.405 | 1 | 1 |