Search Results for stationarity

Abstract

       This study aimed to measure the impact of fair value indicators (net realizable selling value, asset replacement cost, net future cash flows) on investment efficiency as measured by the market value per-share to earnings per share model and the deviation from the expected investment model. The study followed a descriptive analytical approach to interpret the relationship between its variables. The study population consisted of extractive and mining industry companies listed on the Amman Stock Exchange for the fiscal years (2019-2023), totaling (7) companies. To analyze the data and test the hypotheses, the following statistical methods were used: (descriptive statistics, test for stationarity in time series, Durbin-Watson test, Hausman test, multiple linear regression, simple linear regression), relying on the statistical software (EViews).

       The study concluded that there is a positive impact of fair value indicators (net realizable selling value, net future cash flows) on investment efficiency as measured by the market value per-share to earnings per share model, while they had a negative impact on investment efficiency as measured by the deviation from the expected investment model. Additionally, the study found that the asset replacement cost has a positive impact on investment efficiency as measured by the deviation from the expected investment model, and a negative impact on investment efficiency as measured by the market value per-share to earnings per share model.

       Based on the results of this study, the researchers concluded with several recommendations, the most important of which were: the necessity for extractive and mining industry companies listed on the Amman Stock Exchange to expand their disclosure of financial information related to fair value indicators with clarity and transparency to attract investors and gain their trust.

Abstract

This search deals with the use of geographical statistics in estimate proportions of unmeasured pollution with chemical inorganic SO4  in governorates of Iraq except Kurdistan regional,   by using some  univariates  kriging models in spatial prediction by using simple kriging model and ordinary kriging model depending on the measured true data with their coordinates by using ARC10.4.1 and estimate the function value in one point from the close points . by using some semivariogram models (stable, spherical, exponential and Gaussian).and by using the more fitting from them. And by predictive mapping for pollution indications. the comparison was made by five criteria for spatial error indications those are (mean error, root mean square error , mean standardized error ,root mean square standardized error ,average standard error)the study found that ordinary kriging model by using semivariogram function of stable type was the best model for pollution data SO4.the SO4 pollution has been estimated in the un measured points(Ninawa , Salah Al-diyn Al-djyl ,Diyala , Karbala Al-Manfhan , Karbala Muharram eayshih ,Basra Al-Faw) the study showed  that SO4 pollution increase whenever we go towards south of Iraq