Analytical Procedures Phase of PCAOB Audits: A Note of Caution in Selecting the Forecasting Model

Gaber, Mohamed; Lusk, Edward J.

doi:10.11114/afa.v4i1.2811

Cited by 4 publications

(6 citation statements)

References 6 publications

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“…There is a strong inferential indication that the 95%CIs of the RE-Model are too narrow to garner any serious interest in the RE as forecasting model in a challenging context, even if the stringent assumptions for its use were to be founded, 3. There is also a strong inferential indication, albeit idiosyncratic, that the Y:X Excel version has a precision profile that renders its use moot for this accrual set; this was also suggested by Gaber & Lusk (2017)…”

Section: Inferential Impact Re: the Screening Protocolmentioning

confidence: 86%

“…Following is the line-orientation of the {Excel [E], Fixed Effects [F] & Random Effects [R]} CIs. According to Gaber & Lusk (2017) and Lusk (2017), considering the assumptions underlying the production of the CIs, the orientation that one usually expects is:…”

Section: Illustrationmentioning

confidence: 99%

“…The Excel Regression functionality forms a -wide-covering‖ confidence interval. See Lusk (2017) and Gaber & Lusk (2017). These 95%CIs are effectively extreme case CI-scenarios as they are produced from the crisp-end-point parameters of the 95% CI for the intercept and the slope jointly.…”

Section: Olsr Inference From the Excel Parameter Range Modelmentioning

confidence: 99%

“…In Table 4 their RPrecision profiles are presented. (2017) and Gaber & Lusk (2017) studies give preliminary information that the Excel version is likely to produce relatively wide CIs as the Excel-computation uses the extreme case for both parameters in the computation of the CIs. We shall re-consider the utility of this usual assumption in the summary analysis.…”

Section: Anova [Pt [2]] Rprecision Rementioning

confidence: 99%

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OLS-Regression Forecasting Confidence Intervals Capture Rates: Precision Profiling in the Forecasting Model Selection Process

Heilig¹,

Lusk²

2020

IBR

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Forecasting creates projections into an uncertain future. To understand the decision-making implications of the forecast, confidence intervals [CIs] are required. This seems simple enough. However, considering the details of the computations that underlie generating forecasting CIs, one encounters a practical disconnect that can create a decision-making paradox. In the OLS 2-parameter linear regression [OLSR] case, there are two forecasting Models that are often employed: {The Uni-variate Time Series & The Standard two-variable Regression Y:X}. Further, for each of these two Models individually, there are three (1-FPE[α]) %CIs Versions: {Excel, Fixed Effects & Random Effects} each of which is oriented around the same OLSR-forecast value. Given this component configuration, a paradox emerges because each of the forecasting models, {TS or Y:X}, individually produces a forecast with a markedly difference precision profile over the three CI-Versions. In our experience, this is paradoxical as forecasters assume that as the forecasts are the same in each model-class, their Capture Rate-the percentage of time that the actual future values are IN the CIs-should also be the same. To address this seeming paradox, we develop, detail, and illustrate a two-stage OLSR Decomposition and Screening protocol, termed: the [D&S-Triage] protocol that has the following components: (i) Stage A: decomposition of the Model & Version factor-sets to better understand the implications of their Precision differences, and (ii) Stage B: focusing on inferentially significant forecasting model components, create a multilevel quality-algorithm to identify a forecasting model-set that addresses the Quality of the Capture Rate that are the best in their class. 15 During a year-long research project with 12 companies at the leading edge of performance measurement, we devised a -balanced scorecard‖-a set of measures that gives top managers a fast but comprehensive view of the business. The balanced scorecard includes financial measures that tell the results of actions already taken. And it complements the financial measures with operational measures on customer satisfaction, internal processes, and the organization's innovation and improvement activities-operational measures that are the drivers of future financial performance.Of the four pillars of the BSC, the one that we have selected as critical in the forecasting context is the Innovation and Learning Perspective. This begs the critical question: How can the organization continue to improve and create value in the forecasting context? This is the BSC -Learning Loop‖ that we will address in this research report. Simply, the learning loop is the essential way that the organization learns and adapts to focus on measures that have the best chance of providing relevant and reliable Intel to move the organization effectively and efficiently into the future.The simple idea for this BSC-platform is that experimental inferential designs that are used in informing decision-making situations need to be ...

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Section: Inferential Impact Re: the Screening Protocolmentioning

confidence: 86%

Section: Illustrationmentioning

confidence: 99%

Section: Olsr Inference From the Excel Parameter Range Modelmentioning

confidence: 99%

Section: Anova [Pt [2]] Rprecision Rementioning

confidence: 99%

See 2 more Smart Citations

OLS-Regression Forecasting Confidence Intervals Capture Rates: Precision Profiling in the Forecasting Model Selection Process

Heilig¹,

Lusk²

2020

IBR

Self Cite

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show abstract

“…As noted by Gaber & Lusk (2018), the Excel OLSR functionality forms relatively wide-covering confidence intervals compared to the random effects and the fixed effects alternative. Therefore, this model is expected to capture all but the most extreme YE-variants.…”

Section: Caveata: XX Olsr Time Series Inference From the Excel Paramementioning

confidence: 99%

A Vetting Protocol for the Analytical Procedures Platform for the AP-Phase of PCAOB Audits

Gaber¹,

Lusk²

2019

AFR

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Study Context AS5[2017], issued by the Public Company Accounting Oversight Board, requires the use of Analytical Procedures [AP] at the Planning and Substantive Phases of Assurance Audits of firms traded on active exchanges. Logically, an aspect of this requirement is satisfied by using a Panel of the Client’s data at the Planning Phase to forecast the Client’s YE-closing values and then at the Substantive Phase to dispose the directional difference between the: [Actual Client’s YE-value and the AP-Forecasted YE-value]—the Disposition Phase. Research Focus To date, neither the PCAOB nor the AICPA have suggested a pilot-test paradigm to vet the AP-forecasting Protocol under consideration. To address this lacuna, we detail an AP: Decision Support System [AP:DSS] that offers to the Audit InCharge a two-stage pre-analysis AP-vetting [Pilot-Test] platform that employs False Negative [FN] and False Positive [FP] Profilers. In inferential analyses, the FP-Risk is usually benchmarked using the FN-Risk. Deliverables A comprehensive AP-vetting model is offered and illustrated using: (i) a preliminary estimator of a reasonable sample size, (ii) two Standard Forecasting Models: The Excel versions of the OLS Linear Two-parameter and the Moving Average Models, and (iii) a Benchmarking protocol. Unique in this AP:DSS vetting protocol is that the FP-risk is contexted by the FN-risk from the independent benchmark domain. This duality enhances the inferential impact of the vetting protocol as it uses separate variable sets. The AP:DSS is available at no cost as an e-Download.

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Forecasting Confidence Intervals: Sensitivity Respecting Panel-Data Point-Value Replacement Protocols

Heilig¹,

Lusk²

2020

IJAFS

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In the practice of Time Series [TS] forecasting there are very often situations where it is prudent to modify certain “outlier” values in the TS-Panel. A simple modification protocol is to replace selected TS-points by the Average of their adjacent Near-Neighbor-points [ANN]. Thus, a research question, not previously addressed and thus of interest, is: Are ANN-TS modifications balanced—50% of the time provoking OLS-forecasting variation, thus reducing the predicative acuity of the 95% Confidence Intervals; and, by symmetry, 50% of the time smoothing resulting in more predicative resolution. Research Plan To address this question, we: (i) collected accounting information to be forecasted from firms on the Bloomberg™ terminals for Income Statement and Balance Sheet sensitive variables, (ii) formed three ANN-modifications, and (iii) computed 95% Confidence Intervals using the firm-Panel and the three modified Panels. Results Regarding the research question, surprisingly the ANN-replacement protocols were not balanced. In fact, about 2/3 of the time the ANN was smoothing in nature, and thus about 1/3 of the time the ANN-protocol provoked OLS-variation. We discuss the important implication of this result for forecasting in the economic context.

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Analytical Procedures Phase of PCAOB Audits: A Note of Caution in Selecting the Forecasting Model

Cited by 4 publications

References 6 publications

OLS-Regression Forecasting Confidence Intervals Capture Rates: Precision Profiling in the Forecasting Model Selection Process

OLS-Regression Forecasting Confidence Intervals Capture Rates: Precision Profiling in the Forecasting Model Selection Process

A Vetting Protocol for the Analytical Procedures Platform for the AP-Phase of PCAOB Audits

Forecasting Confidence Intervals: Sensitivity Respecting Panel-Data Point-Value Replacement Protocols

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