The new accounting standard requires that financial institutions estimate expected credit losses on their loan portfolios. The predictability of long-term losses, however, remains an open question. We develop a model that predicts long-term loan losses and incorporates adjustments for macroeconomic forecasts. The model combines cross-sectional predictions with a high-dimensional dynamic factor model that tracks aggregate losses over the business cycle. The model predicts long-term losses out-of-sample with significantly greater accuracy than the Harris et al. (2018) model and several other alternatives. It is also more effective at detecting bank failures. We use the model to estimate the present value of expected losses and the expected loss overhang for a given bank-quarter. The estimated present values subsume information in reported allowances and in fair value disclosures about long-term losses; the evidence is also consistent with loss overhang distorting banks' decisions. The model provides a useful benchmark to study loan loss provisioning.

中文翻译：

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预期贷款损失准备金：一个经验模型

新会计准则要求金融机构估计其贷款组合的预期信用损失。然而，长期损失的可预测性仍然是一个悬而未决的问题。我们开发了一个模型来预测长期贷款损失并结合宏观经济预测的调整。该模型将横截面预测与高维动态因子模型相结合，该模型跟踪整个商业周期的总损失。该模型预测样本外的长期损失，其准确性明显高于 Harris 等人的模型。（2018）模型和其他几种替代方案。它在检测银行倒闭方面也更有效。我们使用该模型来估计预期损失的现值和给定银行季度的预期损失。估计的现值包含在报告的备抵和关于长期损失的公允价值披露中的信息；证据也与扭曲银行决策的损失过剩相一致。该模型为研究贷款损失准备金提供了一个有用的基准。