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.
Original languageEnglish
PublisherCornell University - arXiv
Number of pages70
Publication statusPublished - 7 Apr 2021

Publication series


JEL classifications

  • c11 - Bayesian Analysis: General
  • e62 - Fiscal Policy
  • h30 - Fiscal Policies and Behavior of Economic Agents: General
  • z13 - Economic Sociology


  • news
  • fiscal foresight
  • tax shocks
  • identification
  • text mining
  • topic model
  • Latent Dirichlet Allocation

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