.png){kind=icon}
Return
Forecasting using random subspace methods
.png){kind=icon}
OA
AI
DOI：10.1016/j.jeconom.2019.01.009.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Random subspace methods are a new approach to obtain accurate forecasts in high dimensional regression settings. Forecasts are constructed by averaging over forecasts from many submodels generated by random selection or random Gaussian weighting of predictors. This paper derives upper bounds on the asymptotic mean squared forecast error of these strategies, which show that the methods are particularly suitable for macroeconomic forecasting. An empirical application to the FRED -MD data confirms the theoretical findings, and shows random subspace methods to outperform competing methods on key macroeconomic indicators. (C) 2019 Elsevier B.V. All rights reserved.
Keywords:
Dimension reduction
Forecasting
Random subspace
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4
Papers:
5.2K
Citations:
3.0W
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSaveShareSaveShareSaveShareSaveThe Bismuth Subiodides Bi8Pt5I3 and Bi16Pt11I6 – Layered Metals with Covalent Platinum Networks
PREAI
ShareSaveShareSave
View more