دانلود رایگان مقاله لاتین ارزیابی ادبیات سری زمانی مربوط به ارقام حسابداری از سایت الزویر


عنوان فارسی مقاله:

یک ارزیابی انتقادی از ادبیات سری زمانی در حسابداری مربوط به ارقام حسابداری سه ماهه


عنوان انگلیسی مقاله:

A critical assessment of the time-series literature in accounting pertaining to quarterly accounting numbers



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بخشی از مقاله انگلیسی:

The “golden age” of time-series research in accounting occurred more than three decades ago in the late 1970s when researchers initially specified ARIMA models descriptive of quarterly earnings. These descriptive findings supported a dual-process characterization of quarterly earnings comprised of quarter-to-quarter (adjacent) as well as quarter-by-quarter (seasonal) components. Early works by Foster (1977), Griffin (1977), and Brown and Rozeff (1979) provided the support for this generalization. More recent works [i. e., see Bathke, Lorek, & Willinger, 2006; Lorek & Willinger, 2007; Lorek, Willinger, & Bathke, 2008], have extended these findings using more current databases. While seasonality is not present in annual net earnings numbers, disaggregating annual net earnings into quarterly earnings allows researchers to identify potential seasonal characteristics in the data that are masked at the annual level. In fact, Lorek (1979) demonstrated that ARIMA models conditioned upon a quarterly earnings database can be used to generate one-thru-four step ahead quarterly earnings predictions which, when aggregated, yield more accurate annual earnings forecasts than those derived from random-walk models conditioned upon an annual earnings database. Thus, the disaggregation of the annual earnings series enhances predictive ability. The research methodology typically employed in time-series research is to generate firm-specific sample autocorrelation functions (SACFs) across one-thru-twelve lags for sample firms for which a lengthy time-series database of quarterly earnings has been obtained. Firm-specific SACFs are then aggregated across lags and the resulting mean SACFs are analyzed for clues with respect to model structure (i.e., stationarity, appropriate level of differencing, the presence of autoregressive and/or moving-average parameters, etc.). The advantage of this approach vis-à-vis analyzing SACFS on a firm-specific basis is that sampling variation, noise, and measurement error are mitigated. Using this approach, Griffin (1977) conducted a purely descriptive exercise in which he demonstrated that the consecutively and seasonallydifferenced quarterly earnings series exhibits spikes at both the first and fourth lags of the SACF. Such time-series behavior is consistent with the presence of both regular and seasonal moving-average parameters. Using Box–Jenkins notation, Griffin then identified the (011) X (011) ARIMA model for quarterly earnings. His descriptive evidence is consistent with the notion that adjacent and seasonal autocorrelations are present in quarterly earnings data and was instrumental in specifying the aforementioned dual-process characterization. Surprisingly, however, Griffin did not provide any predictive evidence in support of the (011) X (011) ARIMA model



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کلمات کلیدی:

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