EconPapers    
Economics at your fingertips  
 

Forecasting with VAR-teXt and DFM-teXt Models:exploring the predictive power of central bank communication

Leonardo Ferreira ()

No 559, Working Papers Series from Central Bank of Brazil, Research Department

Abstract: This paper explores the complementarity between traditional econometrics and machine learning and applies the resulting model – the VAR-teXt – to central bank communication. The VAR-teXt is a vector autoregressive (VAR) model augmented with information retrieved from text, turned into quantitative data via a Latent Dirichlet Allocation (LDA) model, whereby the number of topics (or textual factors) is chosen based on their predictive performance. A Markov chain Monte Carlo (MCMC) sampling algorithm for the estimation of the VAR-teXt that takes into account the fact that the textual factors are estimates is also provided. The approach is then extended to dynamic factor models (DFM) generating the DFM-teXt. Results show that textual factors based on Federal Open Market Committee (FOMC) statements are indeed useful for forecasting.

Date: 2021-11
New Economics Papers: this item is included in nep-ban, nep-big, nep-cba, nep-cmp, nep-ecm, nep-ets, nep-for, nep-mon and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
https://www.bcb.gov.br/pec/wps/ingl/wps559.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:bcb:wpaper:559

Access Statistics for this paper

More papers in Working Papers Series from Central Bank of Brazil, Research Department
Bibliographic data for series maintained by Rodrigo Barbone Gonzalez ().

 
Page updated 2022-12-02
Handle: RePEc:bcb:wpaper:559