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

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
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