Ryan Thompson, Yilin Qian, Andrey L. Vasnev
arXiv 15 Jul 2022 · Econometrics · publishedOmega (2024) · 5 citations (OpenAlex)
arXiv:2207.07318 · PDF · DOI · OpenAlex · Extracted main text
Forecast combination -- the aggregation of individual forecasts from multiple experts or models -- is a proven approach to economic forecasting. To date, research on economic forecasting has concentrated on local combination methods, which handle separate but related forecasting tasks in isolation. Yet, it has been known for over two decades in the machine learning community that global methods, which exploit task-relatedness, can improve on local methods that ignore it. Motivated by the possibility for improvement, this paper introduces a framework for globally combining forecasts while being flexible to the level of task-relatedness. Through our framework, we develop global versions of several existing forecast combinations. To evaluate the efficacy of these new global forecast combinations, we conduct extensive comparisons using synthetic and real data. Our real data comparisons, which involve forecasts of core economic indicators in the Eurozone, provide empirical evidence that the accuracy of global combinations of economic forecasts can surpass local combinations.
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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 | Matsypura, D., Thompson, R., Vasnev, A.L (2018) Optimal selection of expert forecasts with integer programming self | 1.000 | 7 | 4 | 100% |
| 2 | Bates, J.M., Granger, C.W.J (1969) The combination of forecasts | 1.000 | 5 | 3 | 100% |
| 3 | Conflitti, C., De Mol, C., Giannone, D (2015) Optimal combination of survey forecasts | 0.928 | 4 | 3 | 100% |
| 4 | Radchenko, P., Vasnev, A.L., Wang, W (2023) Too similar to combine? on negative weights in forecast combination self | 0.737 | 3 | 2 | 100% |
| 5 | Montero-Manso, P., Athanasopoulos, G., Hyndman, R.J., Talagala, T.S (2020) FFORMA: Feature-based forecast model averaging | 0.644 | 5 | 2 | 40% |
| 6 | Claeskens, G., Magnus, J.R., Vasnev, A.L., Wang, W (2016) The forecast combination puzzle: A simple theoretical explanation self | 0.644 | 2 | 2 | 100% |
| 7 | Roccazzella, F., Gambetti, P., Vrins, F (2022) Optimal and robust combination of forecasts via constrained optimization and shrinkage | 0.644 | 2 | 2 | 100% |
| 8 | Makridakis, S., Spiliotis, E., Assimakopoulos, V (2020) The M4 Competition: 100,000 time series and 61 forecasting methods | 0.511 | 2 | 2 | 50% |
| 9 | Lawrence, M., Goodwin, P., O'Connor, M., Önkal, D (2006) Judgmental forecasting: A review of progress over the last 25 years | 0.511 | 2 | 1 | 100% |
| 10 | Wang, X., Hyndman, R.J., Li, F., Kang, Y (2023) Forecast combinations: An over 50-year review | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 55 scored citations.