المستخلص:
ان دراسة السلسلة الزمنية تتطلب دقة في وصف ملامح الظاهرة (العملية) التي تتولد منها، سواء من حيث تفسير طبيعة تلك الظاهرة وسلوكها واستخدام النتائج للتنبوء بسلوكها في المستقبل اخذين بنظرالعناية التحقق عما يمكن حدوثه عند تغيير بعض معلمات الانموذج. ويمكن استخ | Studying time series requires an accurate description for the features of phenomena (procedure) that generates time series according to its nature and behavior interpretation. After that, we can use these results for forecasting to the future taking under consideration : examine to what would happen after changing some of the model parameters, use the standard (depended) methods for testing the accuracy of the identified model which has ability for taking errors in the procedure, and provide balancing condition between model diagnosing and simplicity as well as. This research aims to use some of tests concern with diagnosing of the identified model, and make comparisons among these tests meanwhile. The researcher took some of tests, especially, the modern ones, represented by Dm test which depends on partial autocorrelation function, and which depends on autocorrelation function. We use ARMA models with lower order : [AR (1), MA (1), ARMA (1,1)] that Gaussian distribution. Furthermore, we use the tests concern with linear hypothesis diagnosing for the previous three models, represented by squaring the model errors. We use also, the modified tests in this field. In addition, we use some of nonlinear models, as "Threshold" model, and taking self - exciting threshold autoregressive (SETAR) as a special case. The analysis of time series considered time range under model stationary. Methodology of the research depends on both the theoretical part (Statistical theory usage), and the empirical part (Simulation). The structure of the research has mainly based on four chapters as follow : chapter ONE, consists an introduction, the goal of the research and the historical literature. While chapter TWO, covers the statistical methods that take part describing and modeling the time series, as well as explain the main concepts of time series such as "Stationary". We show also, the "mixed" model with lower order, crossing to examining the diagnosing through some of the standard and modern tests which depends on autocorrelation and partial autocorrelation functions of the standardized residual series that obtained from the original series of the model, by testing the randomization of the model errors and make checking for its convenience and accuracy. Furthermore, we use the modified tests : ( , , )and (Kwan & Sim) test represented by ( , , ) to determine the linear hypothesis diagnosing by compute the standardized residual series squares of the error in the previous three models that mentioned above. We use nonlinear model (SETAR) as well as. Chapter THREE, devoted for the empirical part which contains illustrations to all of what mentioned in chapter two. The researcher has designed some of experiments for purpose of comparing the (standard, modern and modified) tests through computing (P - value) for each model at (0.01) significant level at many simulated parameters, with different sample sizes, and for a number of periods of lags at each sample size, by replicating the experiment (1000) times. Finally, chapter FOUR comprises the conclusions and suggestions that the researcher had recommended
اسم المشرف:
احلام احمد جمعة
اسم المؤلف:
سهاد احمد احمد
