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

Tinbergen Institute Discussion Paper 2018-063/III

9 Pages Posted: 20 Aug 2018

See all articles by Lennart F. Hoogerheide

Lennart F. Hoogerheide

VU University Amsterdam

H. K. van Dijk

Tinbergen Institute; Econometric Institute

Date Written: August 8, 2018

Abstract

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.

Keywords: Bayesian learning, predictive density combinations

JEL Classification: C11, C15

Suggested Citation

Hoogerheide, Lennart F. and van Dijk, Herman K., Learning to Average Predictively Over Good and Bad: Comment on: Using Stacking to Average Bayesian Predictive Distributions (August 8, 2018). Tinbergen Institute Discussion Paper 2018-063/III, Available at SSRN: https://ssrn.com/abstract=3228625 or http://dx.doi.org/10.2139/ssrn.3228625

Lennart F. Hoogerheide (Contact Author)

VU University Amsterdam ( email )

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Herman K. Van Dijk

Tinbergen Institute ( email )

Gustav Mahlerplein 117
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Econometric Institute ( email )

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