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We propose a novel text-analytic approach for incorporating textualinformation into structural economic models and apply this to study the effectsof tax news. We first develop a novel semi-supervised two-step topic model thatautomatically extracts specific information regarding future tax policy changesfrom text. We also propose an approach for transforming such textualinformation into an economically meaningful time series to be included in astructural econometric model as variable of interest or instrument. We applyour method to study the effects of fiscal foresight, in particular theinformational content in speeches of the U.S. president about future taxreforms, and find that our semi-supervised topic model can successfully extractinformation about the direction of tax changes. The extracted informationpredicts (exogenous) future tax changes and contains signals that are notpresent in previously considered (narrative) measures of (exogenous) taxchanges. We find that tax news triggers a significant yet delayed response inoutput.

Min(d)ing the President: A text analytic approach to measuring tax news