傳統財務危機預警模型建立，皆是以已通過審核申請者樣本建 立模型，忽略未通過審核申請者樣本，然而以這些樣本所建構出來 的模型，因為不能反映母體的變動程度與變數間的相互影響效果， 亦未考慮樣本選擇偏誤問題，故會影響模型的配適度與預測能力。 本文則是加入拒絕推論技術建立修正後 Heckman 兩階段樣本選擇模 型，以台灣上市公司為例，與傳統財務危機預警模型進行比較；研 究結果發現，財務危機模型建構中的審核模型與違約模型兩階段間 存在顯著之相關，若不採取樣本選擇模型，將對模型預測結果產生 很大的偏誤；而觀察模型的配適度與預測能力後亦可發現，修正後 Heckman 兩階段樣本選擇模型的配適度與預測能力確實優於傳統的 財務危機預警模型。

Traditionally, most scholars use the sample of accepted applicants in building financial distress prediction models and neglect the sample of rejected applicants. These models cannot reflect the variation of population and the interactive effects among all variables, and do not consider the problem of sample selection bias. Therefore, the fitness and prediction ability of the models would be affected. In this paper, the reject inference technology is considered in the financial distress prediction as building the modified Heckman two-stage sample selection model. Using the modified model and Taiwan’s listed companies as examples, we could find that the application and the default stages are highly correlated in distress prediction. In other words, if the modified model is not used, the sample selection bias would result. After observing the fitness and prediction ability of our modified Heckman two-stage sample selection model relative to traditional financial distress prediction models, we discover that the prediction performance of the former is superior.

樣本選擇;拒絕推論;財務危機預警模型;Probit 模型

Sample Selection; Reject Inference; Financial Distress Prediction Model; Probit Model