The Impact of Error-Management Climate, Error Type and Error Originator on Auditors’ Reporting Errors Discovered on Audit Work Papers

We examine factors affecting the auditor’s willingness to report their own or their peers’ self-discovered errors in working papers subsequent to detailed working paper review. Prior research has shown that errors in working papers are detected in the review process; however, such detection rates only rarely exceed 50% of the seeded errors. Hence, measures that encourage auditors to be alert to their own (or their peers’) potential errors any time they revisit the audit working papers may be valuable in detecting such residual errors and potentially correcting them before damage occurs to the audit firm or its client. We hypothesize that three factors affect the auditor’s willingness to report post detailed review discovered errors: the local office error-management climate (open versus blame), the type of error (mechanical versus conceptual) and who committed the error (the individual who committed the error (self) or a peer). Local office error-management climate is said to be open and supportive where errors and mistakes are accepted as part of everyday life as long as they are learned from and not repeated. In alternative, a blame error-management climate focuses on a “get it right the first time” culture where mistakes are not tolerated and blame gets attached to those admitting to or found committing such errors. We find that error-management climate has a significant overall effect on auditor willingness to report errors, as does who committed the error originally. We find both predicted and unpredicted significant interactions among the three factors that qualify these observed significant main effects. We discuss implications for audit practice and further research

Using Internet Data to Account for Special Events in Economic Forecasting

Information about special events can improve economic forecasts substantially. However, due to the lack of timely quantitative data about these events, it has been difficult for professional forecasters to utilise such information in their forecasts. This paper investigates whether Internet search data can improve economic predictions in times of special events. An analysis of 'cash for clunkers' programs in four selected countries exemplifies that including search query data into statistical forecasting models improves the forecasting performance in almost all cases. However, the challenge to identify irregular events and to find the appropriate time series from Google Insights for search remains.Informationen über ungewöhnliche Ereignisse im Prognosezeitraum können die Prognosen ökonomischer Variablen erheblich verbessern. In vielen Fällen liegen aber quantitative Informationen über Ereignisse, wie Steuersatzänderungen oder konjunkturbedingte Ausgabenprogramme, erst mit erheblicher Verzögerung vor. Die Berücksichtigung solcher Ereignisse in der Prognose stellt daher eine große Herausforderung für den Prognostiker dar. In diesem Aufsatz wird untersucht, ob Daten über Suchanfragen im Internet, die zeitnah zur Verfügung stehen, verwendet werden können, um ungewöhnliche Ereignisse bei der Prognose zu berücksichtigen. Dazu werden Suchanfragedaten zu Abwrackprämien-Programmen in vier Ländern untersucht. Die Analyse veranschaulicht, dass Suchanfragedaten die Prognose des Privaten Konsums während der Laufzeit dieser Programme verbessern. Die Herausforderung für den Prognostiker bleibt allerdings, rechtzeitig die richtigen Suchanfragedaten zu finden