EconPapers    
Economics at your fingertips  
 

Truncated priors for tempered hierarchical Dirichlet process vector autoregression

Sergei Seleznev ()
Additional contact information
Sergei Seleznev: Bank of Russia, Russian Federation

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
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
http://cbr.ru/Content/Document/File/84526/wp-47_e.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bkr:wpaper:wps47

Access Statistics for this paper

More papers in Bank of Russia Working Paper Series from Bank of Russia Contact information at EDIRC.
Bibliographic data for series maintained by BoR Research ().

 
Page updated 2020-02-18
Handle: RePEc:bkr:wpaper:wps47