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Univariate Time Series Models [TSM] use only a Panel of historical data to produce forecasts. The tacit belief in using TSM is that the past information portends the future of the longitudinal data-stream. This is likely in certain cases such as strictly Ergodic Panel segments of sufficient size in the overall Panel. A question of interest is: Is the success of TSM in these contexts generalizable? The test of this question used a Litmus-Test design to examine the performance profile of TSM for a longitudinal time series the last point of which is a Turning Point [TP]. Specifically, the inference measure will use the Relative Absolute Error [RAE] of the TSM tested over three forecasting horizons. In this testing, five TSM configurations were employed; the TPs are identified using a fixed screening filter applied to randomly selected firm Panels actively traded on the S&P500 from 2005 through 2013. There is no evidence that any of the five TSM outperformed the RW model which is incidentally the TP. The impact of these results is that one cannot assume that the effectiveness of TSM generalizes to all domains—in particular—forecasting after TPs that seems to be a Domain Lacuna where the effectiveness of TSM will be compromised.

ABSTRACTIn this paper, the author suggests a methodology for facilitating interactive decision making in a decentralized organization by providing information which may result in the transfer of divisional resources. The resource‐transfer information is generated at the beginning of the planning period so that the organization's production control and evaluation systems may be formalized.

A five-step procedure for meta-analysis is used to determine whether the use of return postage in mail surveys yields significantly more responses. Previous research indicates that more expensive return postage has little impact on the rate of return. In contrast, the meta-analysis showed increased gains for first class over buiness reply postage for each of 20 experimental studies previously conducted. The average gain in return was 9.2% (as a % of the original mailout). 4 Tables, 1 Figure, 51 References. Modified AA

AS5[v.Dec:2017] issued by the Public Company Accounting Oversight Board [PCAOB] requires the use of Analytical Procedures [AP] at the Planning and Substantive Phases of Assurance Audits for firms traded on active exchanges. We argue that one aspect of AP, relative to risk-setting, should be vetting the information that is produced/published by the audit client pertaining to Regulation G [v.SEC:2003] called: Non-GAAP information. In our research, we intend to leverage the longstanding Reg[G] requirements to extend the Non-GAAP information to firm performance profiles reported for the Environment, Social, and Governance[ESG]Platform on BloombergÒ. There are two research foci: (1) Offer an AP-Model that uses GAAP & ESG variables to contribute audit evidence useful in making the decision to launch an AP-Extended Procedures examination of the firm's Enterprise Resource Planning & Control [ERP&C] protocols, and (2) Profile a random accrual-set of firms indexed on Bloomberg so as to offer population parameter estimates for refining the AP-Model. The AP-Model is based upon correlational associations for the ESG- & GAAP-variables from the: Income, Balance Sheet & Cash Flow Statements. If there seems to be a disconnect between the nature of these associations for the ESG-variables and those of the GAAP-variables, the auditor may use this as audit evidence in making the decision to conduct an Extended Procedures Examination of the firm's [ERP&C] protocols. As for the other focus, we found that for the accrual of firms tested there is no inferential evidence that the ERP&C-protocols are consistent drivers for both the ESG- and the GAAP variable sets.

ABSTRACTGraduate business education has been criticized for utilizing simplistic teaching strategies that compromise the presentation of real‐world complex skills in the classroom. In this article, we propose that complex management functions can be effectively taught using decomposition strategies. We demonstrate the usefulness of this strategy in the domain of business forecasting by comparing the forecast accuracy of students instructed in decomposition (RBF‐Instructed) with that of students not exposed to such instruction (Uninstructed), as well as with an expert system called Rule‐Based Forecasting (RBF). RBF‐Instructed students demonstrated significant improvements over Uninstructed students and were at least as accurate as the RBF system. Our findings suggest that academics engaged in teaching complex domains can benefit from building their teaching environment on a decomposition framework. We further suggest strategies for measuring the effectiveness and endurance of classroom knowledge thus imparted. Implications for academia and practice are discussed and a research framework is proposed.