Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/297430 
Authors: 
Year of Publication: 
2023
Series/Report no.: 
Bank of Canada Staff Working Paper No. 2023-45
Publisher: 
Bank of Canada, Ottawa
Abstract: 
Bayesian predictive synthesis is a flexible method of combining density predictions. The flexibility comes from the ability to choose an arbitrary synthesis function to combine predictions. I study the choice of synthesis function when combining large numbers of predictions-a common occurrence in macroeconomics. Estimating combination weights with many predictions is difficult, so I consider shrinkage priors and factor modelling techniques to address this problem. The dense weights of factor modelling provide an interesting contrast with the sparse weights implied by shrinkage priors. I find that the sparse weights of shrinkage priors perform well across exercises.
Subjects: 
Econometric and statistical methods
JEL: 
C11
C52
C53
E37
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.