| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w24967 |
| 来源ID | Working Paper 24967 |
| Machine Learning for Regularized Survey Forecast Combination: Partially-Egalitarian Lasso and its Derivatives | |
| Francis X. Diebold; Minchul Shin | |
| 发表日期 | 2018-09-03 |
| 出版年 | 2018 |
| 语种 | 英语 |
| 摘要 | Despite the clear success of forecast combination in many economic environments, several important issues remain incompletely resolved. The issues relate to selection of the set of forecasts to combine, and whether some form of additional regularization (e.g., shrinkage) is desirable. Against this background, and also considering the frequently-found good performance of simple-average combinations, we propose a LASSO-based procedure that sets some combining weights to zero and shrinks the survivors toward equality ("partially-egalitarian LASSO"). Ex-post analysis reveals that the optimal solution has a very simple form: The vast majority of forecasters should be discarded, and the remainder should be averaged. We therefore propose and explore direct subset-averaging procedures motivated by the structure of partially-egalitarian LASSO and the lessons learned, which, unlike LASSO, do not require choice of a tuning parameter. Intriguingly, in an application to the European Central Bank Survey of Professional Forecasters, our procedures outperform simple average and median forecasts – indeed they perform approximately as well as the ex-post best forecaster. |
| 主题 | Econometrics ; Estimation Methods |
| URL | https://www.nber.org/papers/w24967 |
| 来源智库 | National Bureau of Economic Research (United States) |
| 引用统计 | |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/582641 |
| 推荐引用方式 GB/T 7714 | Francis X. Diebold,Minchul Shin. Machine Learning for Regularized Survey Forecast Combination: Partially-Egalitarian Lasso and its Derivatives. 2018. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
| w24967.pdf（378KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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