  We propose a novel text-analytic approach for incorporating textual
information into structural economic models and apply this to study the effects
of tax news. We first develop a novel semi-supervised two-step topic model that
automatically extracts specific information regarding future tax policy changes
from text. We also propose an approach for transforming such textual
information into an economically meaningful time series to be included in a
structural econometric model as variable of interest or instrument. We apply
our method to study the effects of fiscal foresight, in particular the
informational content in speeches of the U.S. president about future tax
reforms, and find that our semi-supervised topic model can successfully extract
information about the direction of tax changes. The extracted information
predicts (exogenous) future tax changes and contains signals that are not
present in previously considered (narrative) measures of (exogenous) tax
changes. We find that tax news triggers a significant yet delayed response in
output.
