Modelling Accounting Populations for Ratio Estimation in Audit Sampling

Abstract

Abstract The classical ratio estimator is one of the auxiliary information estimators frequently discussed in the audit sampling literature. The major weakness of this estimator is its unreliability when accounting populations have only one-sided errors or when the error rate is low. Efforts have been made to improve the classical estimator by using techniques such as the Jackknifed ratio discussed by Frost and Tamura (1982). This paper proposes a new method to estimate the population total error based on the ratio of error over book value, i.e., taintings. The special features of the proposed procedure are that (1) it specifically models the special characteristics of the typical accounting populations, and (2) it is the first study we know of in the audit sampling literature that uses simulation to capture the characteristics of the specific distribution of the estimator each time a confidence interval is constructed. This new approach became possible because of the recent publication of several studies on the empirical characteristics of accounting errors. Results of empirical tests indicate that the proposed method can significantly improve the reliability of the classical ratio under circumstances where the classical ratio needs improvements. Empirical comparisons are also made with a third ratio estimator under dollar-unit sampling. Again, the proposed method provides better reliability.