Estimating and Imputing Missing Tax Loss Carryforward Data to Reduce Measurement Error
针对Compustat数据库中税收亏损结转数据大量缺失的问题，提出一种估计缺失值的方法，替代常见的零值插补，通过10-K数据和已有数据验证其准确性，并重新分析两项研究证明该方法能减少测量误差、提高推断正确性。
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.