طرائـــق بيــز فــي تحليــل نمــوذج القياس الاقتصادي المكاني مع تطبيق عملي == Bayes Methods In Analyzing Spatial Econometrics Model With Practical Application
Author name:
سامي غني خضير عطره
Supervisor name:
خالد ضاري عباس الطائي
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:
07T3705 - p.pdf
Abstract:
ان الخاصية الاساسية لتمييز البيانات المكانية عن بيانات السلاسل الزمنية هي الترتيب المكاني للمشاهدات , وعينات البيانات المكانية تمثل مشاهدات ترتبط بنقاط او مواقع وتتميز هذه البيانات بان ارتباطها يضعف كلما بعدت المسافة وكذلك تتميز بتدرج المكان (( المناطقية | The main property to differentiate the spatial data from time series data is the spatial arrangement of observations and the samples of spatial data represent observations related to points or locations these data are distinguished with the weakness of their connection each time the distance getting far away and the place graduation (location)on the spatial relations of observations are represented in matrix named spatial weights matrix. The spatial econometrics is used in following up the spatial effects like the spatial dependence of observations in different points of place, and spatial heterogeneity arising from relations or parameters model that change with sample data in each time we move within the place , those two points have been neglected in traditional econometrics because of their sinconsistency of statistical assumptions and when taking those spatial effects into consideration ,the statistical influence will be of high efficiency. On the contrary, ignoring these effects will lead to loss the information and will not be as efficient as the dependant sample.Because of the difficulty of deriving the posterior function (on which the Bayesian inference depends) up on applying the Bayesian methods especially in cases that the number of probable models is very large as computing the posterior functions for all these samples requires integration for large dimensions functions which is very difficult Mathematically or inapplicable that requires proper methods can deal with such cases like Gibbs and Metropolis - Hastings sampling by analyzing the posterior distribution to number of conditional distributions for each parameter in the model and then the simplicity of inference for each parameter in the model.The matrix of spatial weights is built by registering the contiguity relations for each location with others in a matrix row (w?) and given (1) if there is a relation between two locations (wij=1) and zero, if it doesn’t (wij=0)as i and j refer to the rows and columns in sequence. For making the summation of each row in the matrix w? equal to 1 , the elements of matrix will be calculated according to the proportion : wij/ ?wij and i=1,2…,n j=1 Consequently, we will get the adjusted matrix W.The practical side was focused on the counting of spatial autoregressive model parameters, and these parameters is the vector ? and it is an ordinary regression parameters vector, and error variance parameter (?2) , and the most important of them is a countig of the parameter (?) which is represent spatial dependance parameter.In order to show the role of the spatial, the practical side included taking the real population for childrens between(1 - 19) year, that had iron deficiency anemia (due to from iron deficiency) that admitted to children’s hospitals in Kurkh district of Baghdad (which had been divided into five geographic regions) for 2010. We have suggested that computing the elements in the adjusted matrix to be on basis of the actual length proportion of the joint borders among different locations that lead to get the accurate estimated value of spatial dependence parameter، where value result of spatial dependence parameter (?) was (0.43) when use Metropolis - Hastings sampling method while value of this parameter when use spatial outoregressive function and by using the adjusted matrix was( 0.57) and value of this parameter when use the same regression function but by using the suggested matrix was (0.85).
