تشخيص وتقدير دالة الانحدار اللامعلمي للبيانات المزدوجة في حالة عدم تحقق بعض فرضياته == The Diagnosis And The Estimation of The Nonparametric Regression Function of The Panal Data In Case Some of Its Hypotheses Are Not Verified
Author name:
دريد حسين بدر
Supervisor name:
ظافر حسين رشيد النجار
General topic:
Administration and Economics
Specific topic:
Statistics
Degree:
Doctorate
University:
University of Baghdad - Faculty Of Administration And Economics - Department Of Statistics
Language:
Arabic
University location:
Baghdad
First pages:
07T3621 - p.pdf
Abstract:
اكتسبت نماذج البيانات المزدوجة اهتماما بالغا وخاصة في الدراسات الاقتصادية والطبية والمالية لانها تاخذ في الاعتبار اثر التغير في الزمن وكذلك اثر التغير في المشاهدات المقطعية على حد سواء في بيانات عينة الدراسة، فضلا عن وصف البيانات من خلال تقدير الانموذج ا | Panel data models have gained a great importance especially in economic, medical, epidemic and financial studies. Because these models take into consideration the impact of the change in time, the impact of the change in sectional views alike inherent in data of a study sample, in addition to describing data through Estimation of the appropriate model. In this thesis, we address the use of method of nonparametric regression in diagnosing and Estimation a model of panel data , as there are specific assumptions related to vector of random errors are not verified. This is because we are going to talk about a nonparametric problem and existence of Heteroscedasiticity and Auto correlated errors which make the process of Estimation wrong, or sometimes not possible. A model has been diagnosed through disclosing all of the problem of Heteroscedasiticity through the use of test (1996) (Zheng) and the problem of Auto correlation by suing test (2013) (Su and Lu). It has been indicated through handling a Nonparametric Hausman Test that the final model adequate for research data is Nonparametric Panel Data Model with Random Effects. Thus, finding Nonparametric Estimator has been tackled through dealing with each problem individually alongside with addressing methods of choosing the smoothing Bandwidth of the model of Random Effects. In case of correlated errors for all techniques of Nonparametric Regression, there are methods to deal with this problem, however all of the said depends critically on addressing estimation methods reliant on finding the choice of an optimal smoothing Bandwidth using more accurate standard until the removal of error process to attain an edited smoothing Bandwidth , of any correlation, is achieved. Then, we could Estimation a model by using Estimation methods. In case of Heteroscedastisity, treatment could be achieved through determining weight by Kernel Estimator, then to be used for the exclusion of the effects of Heteroscedasticity in the study variables through using estimation methods and provision of proposals for classic Nonparametric methods. The formulation of simulated experiments of used models and verification of performance of traditional and proposed methods, for all sample sizes and three levels of standard deviation trough the use of (RAMSE) standard, have been carried out in this thesis. One of the most significant objectives of this study is the selection of the best Estimation method produced by simulation through applying it on a group of balanced Panel Data (longitudinal). This could be conducted through carrying out a practical application to state the effect of the role of gross domestic product on fixed market prices measured in a US Dollar (x) in the state budget measured in millions US Dollars (y) for the period (2003 - 2015). This could be approached through depending on genuine data related to general budget for the Arab States measured by millions US Dollars. The gross domestic product has been focused on since it is the most important economic variable that impacts the budget, as an explanatory variable according to the viewpoint of the competent people for the period (2003 - 2015). The main conclusion in the experimental side is a clear preference in absolute terms to the fortified proposal of Least Square Support Vector Machine for Regression by using an (MGCV) standard on other used Estimation methods. This is in case existence of Auto Correlation as well as provision of a verified proposal for Propose (LCNE), relying on a Span, a selection standard, on other used Estimation methods in case existence of Hetroscedasticity, of all sample sizes, all cases and three levels of three standard deviation. As to practical side, an appropriate model has been diagnosed. Also, compatibility of the best method has been proven in the experimental side alongside with practical one, and the most appropriate for a model by using (RAMSE) standard
