Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/210150
Authors: 
McAlinn, Kenichiro
Aastveit, Knut Are
Nakajima, Jouchi
West, Mike
Year of Publication: 
2019
Series/Report no.: 
Working Paper No. 2/2019
Abstract: 
We present new methodology and a case study in use of a class of Bayesian predictive synthesis (BPS) models for multivariate time series forecasting. This extends the foundational BPS framework to the multivariate setting, with detailed application in the topical and challenging context of multi-step macroeconomic forecasting in a monetary policy setting. BPS evaluates- sequentially and adaptively over time- varying forecast biases and facets of miscalibration of individual forecast densities for multiple time series, and- critically- their time-varying interdependencies. We define BPS methodology for a new class of dynamic multivariate latent factor models implied by BPS theory. Structured dynamic latent factor BPS is here motivated by the application context- sequential forecasting of multiple US macroeconomic time series with forecasts generated from several traditional econometric time series models. The case study highlights the potential of BPS to improve of forecasts of multiple series at multiple forecast horizons, and its use in learning dynamic relationships among forecasting models or agents.
Subjects: 
Bayesian forecasting
agent opinion analysis
dynamic latent factors models
dynamic SURE models
macroeconomic forecasting
multivariate density forecast combination
JEL: 
C11
C15
C53
E37
Persistent Identifier of the first edition: 
ISBN: 
978-82-8379-068-9
Creative Commons License: 
https://creativecommons.org/licenses/by-nc-nd/4.0/deed.no
Document Type: 
Working Paper
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