Revisiting Legislative Lags of U.S. Tax Reforms

P. Adämmer, T. Dybowski
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Abstract

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 exempt municipal bonds and taxable U.S. Treasury securities has broken down in 2007.
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回顾美国税收改革的立法滞后
我们应用无监督机器学习算法来重新审视美国税收改革的立法滞后,并表明至少有两个滞后比之前确定的要长。鉴于免税市政债券和应税美国国债之间的关系在2007年已经破裂,我们的方法提供了另一种近似美国税收预见的方法。
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