Computer-Intensive Methods in Auditing: Bootstrap Difference and Ratio Estimation

24 Pages Posted: 6 Oct 2010 Last revised: 4 Nov 2010

See all articles by Gary C. Biddle

Gary C. Biddle

University of Melbourne - Faculty of Business and Economics; Columbia Business School; HKU Business School; London Business School

Carol M. Bruton

Independent

Andrew F. Siegel

University of Washington - Department of Finance and Business Economics; National Bureau of Economic Research (NBER)

Date Written: June 6, 1990

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.

Suggested Citation

Biddle, Gary C. and Bruton, Carol M. and Siegel, Andrew F., Computer-Intensive Methods in Auditing: Bootstrap Difference and Ratio Estimation (June 6, 1990). Auditing: A Journal of Practice & Theory, Vol. 9, No. 3, 1990, Available at SSRN: https://ssrn.com/abstract=1688154

Gary C. Biddle (Contact Author)

University of Melbourne - Faculty of Business and Economics ( email )

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Carol M. Bruton

Independent

Andrew F. Siegel

University of Washington - Department of Finance and Business Economics ( email )

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United States

National Bureau of Economic Research (NBER)

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