Computer-intensive methods in auditing - bootstrap difference and ratio estimation

Abstract

Computer-intensive methods are a recent development in the theory of statistics with potential applicability in audit and accounting sampling. Whereas traditional sampling approaches can require complex analytics and questionable distributional assumptions, computer-intensive methods generate sampling distributions using simple, though intensive, computer computations.

Generating sampling distributions empirically provides increased versatility, ease of use, and the potential for increased efficiency. However, because computer-intensive methods are sensitive to the representativeness of sample evidence, it becomes an empirical question as to whether they outperform other approaches. This study provides initial evidence on the performance of computer-intensive methods by applying one of them, the bootstrap, to difference and ratio estimation. Tests based on the Neter and Loebbecke [1975] populations reveal the potential for increased efficiency/reliability across a range of sample size and error rate conditions. However, these advantages require an appropriate choice between two versions of the bootstrap. A decision rule for making this choice is hypothesized. While these findings cannot be generalized easily to other populations, they suggest that computer-intensive methods warrant further investigation.