combined with removing the oldest one. This model updating procedure is expected to improve prediction accuracy and anomaly detection capability. [Insert Figure 3 here] V. Anomaly Detection Comparison across CE Models MAPE Comparison performing analytical procedures, auditors use various methods to make predictions on account numbers. It is desirable for expectation models to make forecasts as close to actual values as possible. Many prior AP studies evaluate expectation models in terms of prediction accuracy (Kinney 1978, Wild 1987, Dzeng 1994, Allen et al. 1999, Chen and Leitch 1998, Leitch and Chen 2003). Following this line of research, we compared the prediction accuracies for the CE models in this study using a measure of prediction accuracy called Mean Absolute Percentage Error (MAPE). Additionally, we compared the CE models on their error detection ability. MAPE, the absolute value of the difference between the predicted value and the actual value measured as a percentage of the actual value, is a commonly used metric of prediction accuracy. It is expected that a good model should have a small MAPE. The training set is first used to estimate each of the models. Then, each estimated model is used to make one-step-ahead forecasts and the forecast variance is calculated. After that, the model is updated based on the new data feeds in the hold-out set and the previous steps are repeated. Finally, all the absolute variances are summed up and divided by the total number of observations in the hold-out sample to compute the MAPE. The results for MAPE of Voucher predictions are presented in Table 2. [Insert here] The results indicate that as measured by the MAPE metric the prediction accuracies of models are close. The BVAR version of the VAR model has the best prediction accuracy (MAPE=0.3330), followed by the Subset VAR model (MAPE=0.3374), though the standard deviation for the BVAR model is slightly higher than that of the Subset VAR. The SEM has the lowest prediction accuracy of 0.3499. These prediction accuracies indicate that the forecasts generated by the expectation models usually differ from the reported amounts by approximately 30%.
combined with removing the oldest one. This model updating procedure is expected to improve prediction accuracy and anomaly detection capability. [Insert Figure 3 here] V. Anomaly Detection Comparison across CE Models MAPE Comparison performing analytical procedures, auditors use various methods to make predictions on account numbers. It is desirable for expectation models to make forecasts as close to actual values as possible. Many prior AP studies evaluate expectation models in terms of prediction accuracy (Kinney 1978, Wild 1987, Dzeng 1994, Allen et al. 1999, Chen and Leitch 1998, Leitch and Chen 2003). Following this line of research, we compared the prediction accuracies for the CE models in this study using a measure of prediction accuracy called Mean Absolute Percentage Error (MAPE). Additionally, we compared the CE models on their error detection ability. MAPE, the absolute value of the difference between the predicted value and the actual value measured as a percentage of the actual value, is a commonly used metric of prediction accuracy. It is expected that a good model should have a small MAPE. The training set is first used to estimate each of the models. Then, each estimated model is used to make one-step-ahead forecasts and the forecast variance is calculated. After that, the model is updated based on the new data feeds in the hold-out set and the previous steps are repeated. Finally, all the absolute variances are summed up and divided by the total number of observations in the hold-out sample to compute the MAPE. The results for MAPE of Voucher predictions are presented in Table 2. [Insert here] The results indicate that as measured by the MAPE metric the prediction accuracies of models are close. The BVAR version of the VAR model has the best prediction accuracy (MAPE=0.3330), followed by the Subset VAR model (MAPE=0.3374), though the standard deviation for the BVAR model is slightly higher than that of the Subset VAR. The SEM has the lowest prediction accuracy of 0.3499. These prediction accuracies indicate that the forecasts generated by the expectation models usually differ from the reported amounts by approximately 30%.
Analytical%20Procedures%20for%20Continuous%20Data%20Level%20Auditing%20Continuity%20Equations..docx