Abstract
It is common practice for auditors to verify only a sample of recorded values to estimate the total error amount. Monetary-unit sampling is often used to over-sample large valued items which may be overstated. The aim is to compute an upper confidence bound for the total errors amount. Naïve bounds based on the central limit theorem are not suitable, because the distribution of errors are often very skewed. Auditors frequently use the Stringer bound which known to be too conservative. We propose to use weighted empirical likelihood bounds for Monetary-unit sampling. The approach proposed is different from mainstream empirical likelihood. A Monte–Carlo simulation study highlights the advantage of the proposed approach over the Stringer bound.
Highlights
It is natural to audit only a sample of accounting records to establish the correctness of the entire financial reporting process
Audit techniques are divided into two main areas: the so-called “internal audit” which is carried out internally to monitor the accounting process, and “external audit” carried out by accounting experts who certify the correctness of the accounting recording process
The Stringer bound has been extensively studied in literature and many empirical studies confirm that the coverage level is at least equal to its nominal level
Summary
It is natural to audit only a sample of accounting records to establish the correctness of the entire financial reporting process. Each item in the sample provides the auditor with two types of information: the recorded amount (or book amount) and the audited amount (or corrected amount) The difference between these two amounts is called the error which is used to estimate the overall unknown error amount. Chen et al (2003) proposed an empirical likelihood bound for population containing many zero values This approach is limited to simple random sampling, and cannot be directly used with MUS. Empirical likelihood providing confidence bounds driven by the distribution of the data (Owen 2001); that is, it tends to give large upper bounds with skewed data
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