Methodological Issues Related to the Estimation of Financial Distress Prediction Models
从概念和实证上考察了在非随机样本上估计财务困境预测模型时可能产生的两种估计偏差：过度抽样困境企业导致的基于选择的偏差，以及使用完整数据样本选择标准导致的样本选择偏差。
Researchers typically estimate financial distress prediction models on nonrandom samples. Estimating models on such samples can result in biased parameter and probability estimates if appropriate estimation techniques are not used. This paper examines conceptually and empirically two estimation biases which can result when financial distress models are estimated on nonrandom samples. The first bias results from oversampling distressed firms and falls within the topic of choice-based sample biases. The second results from using a complete data sample selection criterion and falls within the topic of sample selection biases. The two issues examined in this paper arise because of sample selection/data collection constraints typically faced by financial distress researchers. The first constraint is the extremely low frequency rate of firms exhibiting financial distress characteristics (e.g., petitioning for