EconPapers    
Economics at your fingertips  
 

Machine learning for regularized survey forecast combination: Partially-egalitarian LASSO and its derivatives

Francis X. Diebold and Minchul Shin

International Journal of Forecasting, 2019, vol. 35, issue 4, 1679-1691

Abstract: Despite the clear success of forecast combination in many economic environments, several important issues remain incompletely resolved. The issues relate to the 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 that are motivated by the structure of partially-egalitarian LASSO and the lessons learned, which, unlike LASSO, do not require the 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.

Keywords: Forecast combination; Forecast surveys; Shrinkage; Model selection; LASSO; Regularization (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3) Track citations by RSS feed

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0169207018301596
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:35:y:2019:i:4:p:1679-1691

DOI: 10.1016/j.ijforecast.2018.09.006

Access Statistics for this article

International Journal of Forecasting is currently edited by R. J. Hyndman

More articles in International Journal of Forecasting from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().

 
Page updated 2020-02-08
Handle: RePEc:eee:intfor:v:35:y:2019:i:4:p:1679-1691