درخت حوزه‌های تخصصی

Since calculating the amount of fractal in the ARFIMA time series and increasing its accuracy and bring it closer to reality is very important, this article intends to investigate the possibility of modifying this computational formula by changing the focus criterion and using simulation. In the present paper, by analysing and simulating the fractal parameter for time series ARFIMA model and redefining and reviewing the Fractal mathematical, a fractal calculus and dimension in comparison with Euclidean norms introduced. In this regard, first, a new criterion about fractal or Hausdorff component for measuring the forms of fractal time series introduced, then the effects and functional inquiries using simulation data searched, and some mathematical proofs through simulation of data achieved. The findings showed that, the deviation of the new estimator from the simulated initial value is less, and closer to reality as this new criterion introduced by changing the focus criterion and replacing the mean with the median due to less sensitivity to out-dated data. The new criterion is better for determining the fractal parameter and identifying its degree of effectiveness. Finally, the findings empirically indicated that the proposed criterion is more efficient and better than the others for calculating fractal dimensions.

The emergence of a new theory of"macroaccounting" with a new wave of accounting research over the last decade tries to explain and use the Aggregate information of interim accounting statements in economic forecasts. Macroaccounting theory suggests that economists and macroeconomic forecasters use Aggregate accounting information at the macroeconomic level. For example, accounting earning is used to predict GDP, cost stickiness is used to predict unemployment, and the ratio of book value to market value is used to predict inflation. Earnings growth dispersion contains information about trends in labor reallocation, unemployment change, and, ultimately, aggregate output. initial macroeconomic estimates released by the Central bank of Islamic Republic of Iran and Planning and Budget Organization and Statistical Center of Iran do not fully incorporate this information. Accordingly, the present study, based on macroaccounting theory, has examined the Predicting Restatements in Macroeconomic Indicators using Accounting Information. The population of this study includes all companies listed in the TSE. Due to the seasonality of the data and the fit of the models in a time series, the observations reach 40 times (2008:1to2018:4). The research method is based on time series data, VARtechnique. the The results suggest that earnings growth dispersion provides related data about final GDP growth. The results suggest that after considering the effect of other influential factors, specifically real initial GDP, earnings growth dispersion is useful in forecasting future GDP changes. The findings are important for economists and policymakers to have more accurate economic estimation and prediction by applying for accounting Information.