Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/185588 
Year of Publication: 
2018
Series/Report no.: 
Tinbergen Institute Discussion Paper No. TI 2018-069/III
Publisher: 
Tinbergen Institute, Amsterdam and Rotterdam
Abstract: 
Increasingly, professional forecasters and academic researchers present model-based and subjective or judgment-based forecasts in economics which are accompanied by some measure of uncertainty. In its most complete form this measure is a probability density function for future values of the variables of interest. At the same time combinations of forecast densities are being used in order to integrate information coming from several sources like experts, models and large micro-data sets. Given this increased relevance of forecast density combinations, the genesis and evolution of this approach, both inside and outside economics, is explored. A fundamental density combination equation is specified which shows that various frequentist as well as Bayesian approaches give different specific contents to this density. In its most simplistic case, it is a restricted finite mixture, giving fixed equal weights to the various individual densities. The specification of the fundamental density combination is made more flexible in recent literature. It has evolved from using simple average weights to optimized weights and then to `richer' procedures that allow for time-variation, learning features and model incompleteness. The recent history and evolution of forecast density combination methods, together with their potential and benefits, are illustrated in a policy making environment of central banks.
Subjects: 
Forecasting
Model Uncertainty
Density Combinations
JEL: 
C10
C11
Document Type: 
Working Paper

Files in This Item:
File
Size
721.08 kB





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