應用核密度函數估計法挑選信用風險模式的解釋變數

Variable Selection for Credit Risk Models Using Kernel Density Estimation

10.29973/JCSA.200812.0002

黃瑞卿(Ruey-Ching Hwang)；蕭兆祥(Jhao-Siang Siao)；吳志強(Chih-Chiang Wu)

判別分析模式 ； 核密度函數估計 ； 羅吉斯模式 ； 樣本外誤差率 ； Discriminant analysis model ； kernel density estimator ； logistic model ； out-of-sample error rate

中國統計學報

46卷4期（2008 / 12 / 01）

269 - 287

繁體中文

本文使用核密度函數估計法計算每個解釋變數區別破產公司與正常公司的能力，將所有考慮的解釋變數按區別能力優劣排序分組。然後，給定各組解釋變數，分別應用在修正判別分析模式(modified discriminant analysis model; Welch, 1939)與羅吉斯模式(logistic model; Ohlson, 1980)。實證研究結果顯示，使用區別能力愈好的解釋變數組合，不論在修正判別分析模式或羅吉斯模式，均有較小的樣本外誤差率。

In this paper, the kernel density estimator is applied to choose the explanatory variables for credit risk models. For doing it, given each considered explanatory variable, its ability on discriminating between healthy and bankrupt companies is measured using its one-dimensional kernel density estimate. All considered explanatory variables are then separated into groups according to the magnitude of their discriminant abilities. The prediction performance of both credit risk models, the modified discriminant analysis model (Welch, 1939) and the logistic model (Ohlson, 1980), is compared on each group of explanatory variables. Empirical studies demonstrate that the two credit risk models using explanatory variables of better discriminant ability have better prediction performance, in the sense of yielding smaller out-of-sample error rates.

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