Truncated priors for tempered hierarchical Dirichlet process vector autoregression

Sergei Seleznev

No wps47, Bank of Russia Working Paper Series from Bank of Russia

Abstract: We construct priors for the tempered hierarchical Dirichlet process vector autoregression model (tHDP-VAR) that in practice do not lead to explosive forecasting dynamics. Additionally, we show that tHDP-VAR and its variational Bayesian approximation with heuristics demonstrate competitive or even better forecasting performance on US and Russian datasets.

Keywords: Bayesian nonparametrics; forecasting; hierarchical Dirichlet process; infinite hidden Markov model. (search for similar items in EconPapers)
JEL-codes: C11 C32 C53 E37 (search for similar items in EconPapers)
Pages: 37 pages
Date: 2019-10
New Economics Papers: this item is included in nep-cis, nep-ecm, nep-ets, nep-mac and nep-ore
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