Recent empirical research emphasizes the importance of foresight for tax policy analyses. According to Leeper et al. (2013a), failing to model foresight adequately can lead to biased inference in empirical models. The authors reveal this bias by augmenting the SVAR model of Blanchard and Perotti...

We combine a probabilistic topic model and a dictionary-based sentiment analysis to construct a time series, which indicates when and how (positive vs. negative) the U.S. president communicates his tax policy news to the public. The econometric analyses show that optimistic tax policy statements...

We apply an unsupervised machine learning algorithm to revisit legislative lags of U.S. tax reforms and show that at least two lags have been longer than previously identified. Our approach offers an alternative way to approximate U.S. tax foresight, given that the relationship between tax...