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Cherry picking words: a topic model application to ecb speeches
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| 2.07 MB | Adobe PDF |
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Orientador(es)
Resumo(s)
Communication is set to become vital for policy
transmission. Growing textual data availability requires
the development of more sophisticated tools. This report
investigated the empirical application of Latent Dirichlet
Allocation (LDA), a Machine Learning Topic Model, with
the aim of revealing topics in ECB speeches for the period
1997-2022. Results corroborate comparable studies and
produce significant novelties: Topic dynamics captured
structural macroeconomic events; ECB speakers differ in
topic allocation and frequency coverage; Topic weights
correlate with underlying variables and the model well
perform on unseen documents. However, outcomes should
be treated with caution as LDA lies between correlation
and causality.
Descrição
Palavras-chave
Machine learning Ecb Topic modeling Lda model Central bank communication