We suggest to extend the stacking procedure for a combination of predictive densities, proposed by Yao et al in the journal Bayesian Analysis to a setting where dynamic learning occurs about features of predictive densities of possibly misspecified models. This improves the averaging process of good and bad model forecasts. We summarise how this learning is done in economics and finance using mixtures. We also show that our proposal can be extended to combining forecasts and policies. The technical tools necessary for the implementation refer to filtering methods from nonlinear time series and we show their connection with machine learning. We illustrate our suggestion using results from Basturk et al based on financial data about US portfolios from 1928 until 2015.
# 18-063/III (2018-08-08)
- Lennart (L.F.) Hoogerheide, VU University Amsterdam; Herman (H.K.) van Dijk, Erasmus University, Norges Bank
- Bayesian learning, predictive density combinations
- JEL codes:
- C11, C15