Learning to Average Predictively over Good and Bad: Comment on: Using Stacking to Average Bayesian Predictive Distributions

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Tinbergen Institute Discussion Paper ; No. TI 2018-063/III

Classification and Topics

• Classification: Wirtschaft ; Bayesian Analysis: General ; Statistical Simulation Methods: General
• Subject: Bayesian learning ; predictive density combinations

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Hoogerheide, Lennart ; van Dijk, Herman
• Event: Veröffentlichung
• (who): Tinbergen Institute
• (where): Amsterdam and Rotterdam
• (when): 2018

Further information

• Handle: http://hdl.handle.net/10419/185582
• Last update: 10.03.2025, 11:52 AM CET