SSE/EFI Working Paper Series in Business Administration
No 2005:13:
On the Choice-Based Sample Bias in Probabilistic Business Failure Prediction
Kenth Skogsvik ()
Abstract: Probabilistic business failure prediction models are
commonly estimated from non-random samples of companies. The proportion of
failure companies in such samples is often much larger than the proportion
of failure companies in most real-world decision contexts. This so-called
“choice-based sample bias” implies that calculated failure probabilities
will be (more or less) biased. The purpose of the paper is to analyse this
bias and its consequences for standard applications of probabilistic
failure prediction models (for example probit/logit analysis) and in
particular to investigate whether the bias can be eliminated without having
to re-estimate the underlying statistical model. It is shown that there is
a straightforward linkage between sample-based probabilities of failure and
the corresponding population-based probabilities. Knowing this linkage,
sample-based probabilities can be adjusted for the “choice-based sample
bias”, provided that sufficiently large samples of randomly selected
failure companies and randomly selected survival companies have been used
in the estimation of the underlying statistical model. Empirical
observations in previous research are in line with the theoretical results
of the paper.
Keywords: Business Failure Prediction; Choice-Based Sample Bias; Financial Analysis; Probabilistic Prediction Model; Probit/Logit Analysis; (follow links to similar papers)
18 pages, December 1, 2005, Revised January 9, 2006
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