  This paper studies the content of central bank speech communication from 1997
through 2020 and asks the following questions: (i) What global topics do
central banks talk about? (ii) How do these topics evolve over time? I turn to
natural language processing, and more specifically Dynamic Topic Models, to
answer these questions. The analysis consists of an aggregate study of nine
major central banks and a case study of the Federal Reserve, which allows for
region specific control variables. I show that: (i) Central banks address a
broad range of topics. (ii) The topics are well captured by Dynamic Topic
Models. (iii) The global topics exhibit strong and significant autoregressive
properties not easily explained by financial control variables.
