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Flexible global forecast combinations (2207.07318v3)

Published 15 Jul 2022 in econ.EM

Abstract: 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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References (55)
  1. Persistence in forecasting performance and conditional combination strategies. Journal of Econometrics 135, 31–53.
  2. Matrix: Sparse and dense matrix classes and methods. URL: https://CRAN.R-project.org/package=Matrix. R package version 1.5-3.
  3. The combination of forecasts. OR 20, 451–468.
  4. A note on the validity of cross-validation for evaluating autoregressive time series prediction. Computational Statistics and Data Analysis 120, 70–83.
  5. Bias-variance trade-off and shrinkage of weights in forecast combination. Management Science 66, 5720–5737.
  6. A nonparametric approach to identifying a subset of forecasters that outperforms the simple average. Empirical Economics 53, 101–115.
  7. Forecast combination with entry and exit of experts. Journal of Business and Economic Statistics 27, 428–440.
  8. Multitask learning. Machine Learning 28, 41–75.
  9. The forecast combination puzzle: A simple theoretical explanation. International Journal of Forecasting 32, 754–762.
  10. Optimal combination of survey forecasts. International Journal of Forecasting 31, 1096–1103.
  11. Machine learning for regularized survey forecast combination: Partially-egalitarian lasso and its derivatives. International Journal of Forecasting 35, 1679–1691.
  12. Learning multiple tasks with kernel methods. Journal of Machine Learning Research 6, 615–637.
  13. Regularized multi–task learning, in: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 109–117.
  14. Solving the forecast combination puzzle arXiv:2308.05263.
  15. Combining expert forecasts: Can anything beat the simple average? International Journal of Forecasting 29, 108–121.
  16. Optimal prediction pools. Journal of Econometrics 164, 130–141.
  17. Conditionally optimal weights and forward-looking approaches to combining forecasts URL: https://christopherggibbs.weebly.com/uploads/3/8/2/6/38260553/cow_2021_forweb.pdf.
  18. Ensembles of localised models for time series forecasting. Knowledge-Based Systems 233, 107518.
  19. Improved methods of combining forecasts. Journal of Forecasting 3, 197–204.
  20. Gurobi Optimizer reference manual. URL: https://www.gurobi.com.
  21. Combining density forecasts. International Journal of Forecasting 23, 1–13.
  22. Least-squares forecast averaging. Journal of Econometrics 146, 342–350.
  23. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics 12, 55–67.
  24. forecast: Forecasting functions for time series and linear models. URL: https://pkg.robjhyndman.com/forecast/. R package version 8.21.
  25. Another look at forecast selection and combination: Evidence from forecast pooling. International Journal of Production Economics 209, 226–235.
  26. Retrieval and analysis of Eurostat open data with the eurostat package. R Journal 9, 385–392.
  27. Time-series extreme event forecasting with neural networks at Uber, in: ICML 2017 Time Series Workshop.
  28. Judgmental forecasting: A review of progress over the last 25 years. International Journal of Forecasting 22, 493–518.
  29. A well-conditioned estimator for large-dimensional covariance matrices. Journal of Multivariate Analysis 88, 365–411.
  30. Introduction to global optimization. Technical Report. URL: https://www.lix.polytechnique.fr/~liberti/teaching/globalopt-lima.pdf.
  31. On the uncertainty of a combined forecast: The critical role of correlation. International Journal of Forecasting 39, 1895–1908.
  32. The M4 Competition: 100,000 time series and 61 forecasting methods. International Journal of Forecasting 36, 54–74.
  33. Optimal selection of expert forecasts with integer programming. Omega 78, 165–175.
  34. FFORMA: Feature-based forecast model averaging. International Journal of Forecasting 36, 86–92.
  35. Principles and algorithms for forecasting groups of time series: Locality and globality. International Journal of Forecasting 37, 1632–1653.
  36. Experience with forecasting univariate time series and the combination of forecasts. Journal of the Royal Statistical Society: Series A (General) 137, 131–165.
  37. Potential GNP: Its measurement and significance, in: Proceedings of the Business and Economic Statistics Section of the American Statistical Association.
  38. High moment constraints for predictive density combinations URL: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3593124.
  39. ecb: Programmatic access to the European Central Bank’s Statistical Data Warehouse. URL: https://CRAN.R-project.org/package=ecb. R package version 0.4.1.
  40. Judgmental selection of forecasting models. Journal of Operations Management 60, 34–46.
  41. The relation between unemployment and the rate of change of money wage rates in the united kingdom, 1861–1957. Economica 25, 283–299.
  42. Forecast combination through dimension reduction techniques. International Journal of Forecasting 27, 224–237.
  43. Combining forecasts for universally optimal performance. International Journal of Forecasting 38, 193–208.
  44. R Core Team, 2023. R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria. URL: https://www.R-project.org/.
  45. Too similar to combine? on negative weights in forecast combination. International Journal of Forecasting 39, 18–38.
  46. Optimal and robust combination of forecasts via constrained optimization and shrinkage. International Journal of Forecasting 38, 97–116.
  47. Forecasting in the presence of instabilities: How we know whether models predict well and how to improve them. Journal of Economic Literature 59, 1135–1190.
  48. DeepAR: Probabilistic forecasting with autoregressive recurrent networks. International Journal of Forecasting 36, 1181–1191.
  49. A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics. Statistical Applications in Genetics and Molecular Biology 4.
  50. Combination forecasts of output growth in a seven-country data set. Journal of Forecasting 23, 405–430.
  51. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological) 58, 267–288.
  52. Nonparametric Stein-type shrinkage covariance matrix estimators in high-dimensional settings. Computational Statistics and Data Analysis 83, 251–261.
  53. Forecast combinations: An over 50-year review. International Journal of Forecasting 39, 1518–1547.
  54. Combining forecasting procedures: Some theoretical results. Econometric Theory 20, 176–222.
  55. A survey on multi-task learning. IEEE Transactions on Knowledge and Data Engineering 34, 5586–5609.
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