The ability to reduce current and future taxable income with prior years' taxable losses is highly relevant for explaining firms' effective tax rates. Compustat data on the tax loss carryforward (TLCF) are, however, often missing. We propose a method to estimate values for the missing TLCF data instead of the common practice in the literature of imputing zero values. In order to assess the accuracy of our method, we compare our estimated TLCFs with both a random selection of 10-K data and Compustat data for firm-years where Compustat data is available. The results show that our estimated values align very closely with the reported data. We re-analyze two existing studies using these estimated values. With the first, we show that imputing our estimated values instead of zeros leads to a large decrease in measurement error. This reduces the risk that firms with missing data and low effective tax rates are incorrectly classified as tax aggressive. The second re-analysis shows that using our estimated TLCFs leads to economically and statistically different conclusions compared to imputing zeros. Using our estimated values thus increases the probability of correct inferences in studies that use Compustat TLCF data. The estimated values are available from https://doi.org/10.34894/N9J1WE.

用前几年的应税亏损减少当前和未来应税收入的能力与解释公司的有效税率高度相关。然而，关于税收损失结转 (TLCF) 的 Compustat 数据经常丢失。我们提出了一种方法来估计缺失的 TLCF 数据的值，而不是文献中常见的估算零值的做法。为了评估我们的方法的准确性，我们将我们估计的 TLCF 与随机选择的 10-K 数据和公司年份的 Compustat 数据进行比较，其中 Compustat 数据是可用的。结果表明，我们的估计值与报告的数据非常吻合。我们使用这些估计值重新分析了两项现有研究。对于第一个，我们表明将我们的估计值而不是零值进行估算会导致测量误差大大减少。这降低了将数据缺失和有效税率低的公司错误地归类为税收侵略性的风险。第二次重新分析表明，与插补零相比，使用我们估计的 TLCF 会导致经济和统计上不同的结论。因此，使用我们的估计值会增加使用 Compustat TLCF 数据的研究中正确推断的可能性。估计值可从 https://doi.org/10.34894/N9J1WE 获得。