英文文献：Forecasting Net Charge-Off Rates of Banks: A PLS Approach-预测银行净销账率:一种PLS方法
英文文献作者：James Barth,Sunghoon Joo,Hyeongwoo Kim,Kang Bok Lee,Stevan Maglic,Xuan Shen
英文文献摘要：
This chapter relies on a factor-based forecasting model for net charge-off rates of banks in a data-rich environment. More specifically, we employ a partial least squares (PLS) method to extract target-specific factors and find that it outperforms the principal component approach in-sample by construction. Further, we apply PLS to out-of-sample forecasting exercises for aggregate bank net charge-off rates on various loans as well as for similar individual bank rates using over 250 quarterly macroeconomic data from 1987Q1 to 2016Q4. Our empirical results demonstrate superior performance of PLS over benchmark models, including both a stationary autoregressive type model and a nonstationary random walk model. Our approach can help banks identify important variables that contribute to bank losses so that they are better able to contain losses to manageable levels.

本章基于基于因子的预测模型，在数据丰富的环境下对银行的净销账率进行预测。更具体地说，我们使用偏最小二乘(PLS)方法来提取目标特定因子，并发现它优于样本内构造的主成分方法。此外，我们将PLS应用于样本外预测练习，用于各种贷款的银行总净冲销利率，以及使用1987年第一季度到2016年第4季度超过250个季度的宏观经济数据用于类似的单个银行利率。我们的经验结果表明，PLS模型优于基准模型，包括一个平稳自回归型模型和一个非平稳随机游走模型。我们的方法可以帮助银行识别导致银行损失的重要变量，以便他们能够更好地将损失控制在可管理的水平。