2023-11

مجلة العلوم الانسانية لجامعة HJUOZ زاخو https://doi.org/10.26436/hjuoz.2023.11.4.1193 (Issue : 11) (Volume : 4)

الملخص:أن اساس العلاقة بين الموازنة العامة والاحتياطيات الاجنبية هو موقف الحساب التجاري من الايرادات النفطية، حيثالاختلال الداخلي يتمثل بعجز الموازنة العامة، مما يؤدي الى التمويل عن طريق الاقتراض بصورة مباشرة من البنك المركزي، او غير مباشرة عن طريق خصم اوراق الدين لتمويل ذلك العجز،وبكل تاكيد ترتبط تلك العملية بالاحتياطيات الاجنبية من خلال نافذة بيع العملة، وذلك لمعالجة العجز المزدوج في الحساب الجاري والمعطوف على عجز الموازنة العامة ، لذلك يمثل الاتجاه العام لعلاقة الانفاق الحكومي بالاحتياطيات الاجنبية من المواضيع الاساسية ذات التاثير الاكبر بكل مكونات الناتج المحلي الاجمالي وعملية التنبؤ لنماذج التمهيد الاسي والمقارنة بينهما، لتلك العلاقة تمثل جوهر الاثرلتلك المكونات ، اذ ان أستخدامبيانات السلسلة الزمنية للأنفاق الحكومي والاحتياطات الاجنبية للمدة من (2004-2020) بإعتماد نماذج التمهيد الأسي،ولما تمتاز بههذه النماذج من دقة ومرونة عالية في تحليل السلاسل الزمنية . واظهرت نتائج التطبيق أن الأنموذج الملائم والامثل لتمثيل بيانات السلسلة الزمنية، هوأنموذج التمهيدي الأسي المزدوج (طريقة هولت) لقدرتها على معالجة السلاسل الزمنية المحتواة على مركبة الاتجاه العام الغير موسمي ، حيث لوحظ من بيانات السلسلة الزمنية للأنفاق الحكومي والاحتياطات الاجنبية في العراق للمدة من (2004-2020 )بان هنالك اتجاه عام متزايد غير موسمي، ووفقا لمعيار متوسط مربع الخطأ الخطأ((MSD) or (MSE) )بعد تحديد معالم التمهيد المثلى من أجل الحصول على تنبؤات مستقبلية للأنفاق الحكومي والاحتياطات الاجنبية في العراق للمدة من (2021-2030 ، )حيث أظهرت هذه القيم تناسقا مع مثيلاتها في السلسلة الزمنية الاصلية.وبعد أجراء المقارنة تبين بان أنموذج التمهيدي الأسي المزدوج (طريقة هولت) ، قد اعطت مؤشرات اقل من مؤشرات أنموذج التمهيدي الأسي المزدوج (طريقة هولت) للأنفاق الحكومي في العراق ،الامر الذي يشير بوضوح بأنه الأنموذج الملائم والاكفأ لتقدير التنبؤات المستقبليةللمدة من (2021-2030.)الكلمات الدالة:التمهيد الأسي الأحادي ، التمهيد الأسي المزدوج (طريقة هولت) ،التمهيد الأسي الثلاثي (طريقة هولت ونترز)،الأنفاق الحكومي ،

2023-06

Al Kut Journal of Economics and Administrative Sciences (Issue : 47) (Volume : 15)

In this paper , the time series data of the daily exchange rate of the euro against the dollar were used for the period from (12/7/2020 H:1 AM-10/12/2020 H:7 AM) with a total of (79) Observations as (120) Observations for comparison with the forecast values obtained using the two methods, to make the comparison process for future forecasts for the period from (10/12/2020 H:8 AM- 15/12/2020 H:7 AM) with a total of (120) Observations. To predict them, between the double exponential smoothing model (Holt method) and the feed-forward artificial neural network model using the two algorithms (Incremental Back Propagation algorithm, Quick Propagation algorithm).These methods are characterized by high accuracy and flexibility of these methods in the process of analyzing the time series, the results of the application showed that the most efficient and optimal model for representing the time series data is the artificial neural network model [2-10-1] using the Quick Propagation algorithm for the daily exchange rate of the euro against the dollar according to the criterion The mean square error ( MSE), has given lower indicators than the indicators of the artificial neural network model [2-10-1] using the Incremental Back Propagation algorithm, and the double exponential smoothing model (Holt method) when using (α =0.9 and = 0.1), which clearly indicates that it is the appropriate and efficient model for estimating future forecasts for the period from (10/12/2020 H:8 AM- 15/12/2020 H:7 AM). Where these values showed consistency with their counterparts in the original series, and provided us with a future picture of the reality of the daily exchange rate of the euro against the dollar for that period. Therefore, the artificial neural network model provided better future predictions than those provided by the double exponential smoothing model (Holt method), according to the standard of mean square error ( MSE), it gave less indicators than the indicators of the Holt model. The ready-made statistical programs MinitabV18 were used in the statistical side, and the ready-made neural network system program known as Alyuda NeuroIntelligence was used in the neural networks side. Keywords: double exponential smoothing (Holt's method), feed-forward artificial neural networks, daily exchange rate of EUR/USD

2019-03

مجلة تكريت الادارية والاقتصادية (Issue : 15) (Volume : 45)

With the enormous scientific progress that technology is witnessing at the moment, new types of systems have emerged called smart systems. That have been developed and used in many current applications Genetic algorithms. Were used in this research to study the distribution of Boltzmann, which is subject to the composition of ribosomal RNA, and included the suggestion of a genetic algorithm that combines the method of the maximum likelihood with the method of generation constraint to estimate the Boltzmann distribution parameter represented by the RNA parameterThe results showed that the embedded genetic algorithm is better for estimating the RNA string parameter than previous methods. Matlab has been used in writing research algorithms and finding results. Keywords: genetic algorithm, Boltzmann distribution, Maximum Likelihood, generation constraint

2018-12

مجلة تكريت الادارية والاقتصادية (Issue : 4) (Volume : 44)

This paper has used the time series data for the Iraqi dinar Exchange Rate against the American dollar for the period (2003-2014) by using the Exponential Smoothing models (Single Exponential Smoothing model and Double Exponential Smoothing model "Holt's Method"). The reason of using these models is because the high accuracy and flexible in analyzing the time series data. The results of application show that the proper and efficient model for representing time series data is: - The Double Exponential Smoothing model according to MSE measure after determine the optimal smoothing parameter to obtain future forecasting for the on the Iraqi dinar Exchange Rates against the American dollar for the period (2015-2020), these values showed a harmonic direction with the same original time series- Double Exponential Smoothing (Holt’s method) has the ability to deal with the time series data that contains no seasonal pattern for the Iraqi dinar Exchange Rate against the American dollar for the period (2003-2014). The results showed that there is trend of non-seasonal decreasing trend. Minitab program has been considered for the statistical analysis. Keywords: Single Exponential Smoothing, Double Exponential Smoothing, Exchange Rate, time series analysis, Mean Square Error.

2018-02

Journal of Applied Statistics (Issue : 14) (Volume : 45)

In many real applications, such as econometrics, biological sciences, radio-immunoassay, finance, and medicine, the usual assumption of constant error variance may be unrealistic. Ignoring heteroscedasticity (non-constant error variance), if it is present in the data, may lead to incorrect inferences and inefficient estimation. In this paper, a simple and effcient Gibbs sampling algorithm is proposed, based on a heteroscedastic linear regression model with an l1 penalty. Then, a Bayesian stochastic search variable selection method is proposed for subset selection. Simulations and real data examples are used to compare the performance of the proposed methods with other existing methods. The results indicate that the proposal performs well in the simulations and real data examples. R code is available upon request.

2018-01

مجلة تكريت الادارية والاقتصادية (Issue : 2) (Volume : 42)

This research aimed to preview to what extent of operating the operated banks in Kurdistan region to the concept of intellectual capital and its components , and how it is reflecting on their gaining of competitive advantage, and also to show to what extent the bank under study recognize the components of Intellectual capital and how that effects their gaining to the competitive advantage . The research assumes several hypotheses, however the most important thing is that there is a correlation and effect relationship between the intellectual capital and its components and the competitive advantage and its dimensions in some commercial banks in Iraqi Kurdistan Region. This research took place in Kurdistan region by dealing with some of its banks as a research samples both governmental and private banks. It included managers and the assistants of mangers and the heads of departments (147) as a sample from (39) banks . The researcher depended on questionnaire to collect information then statistic analyses was made on statistic program ( SPSS.v.17). The researcher found several conclusions most important thing is that there is non a correlation and effect relationship between the intellectual capital and its components and the competitive advantage and its dimensions . The research ended with some recommendations that it is necessary to make, and to try promote the intellectual capital philosophy among the employees in the bank according to some basis and that could help the banks to achieve a competitive advantage.

2017-08

Academic Journal of Nawroz University (Issue : 6) (Volume : 3)

Support vector machine initially developed to perform binary classification. This paper presents a multi-class support vector machine classifier and ordinal regression to classify the type of bone mineral density. This paper compares the performance of four multi-class approaches, one-against-all, one-against-one, Weston and Watkins, and Crammer and Singer. Results from our real life data conclude that Crammer and Singer may be better approach depending on training error and the percentage of correctly classified test data. Also, we fined that the training error become more less when the regulization parameter C and kernel parameter  become large.

2017-07

International Journal of Advanced Statistics and Probability (Issue : 5) (Volume : 2)

Over the past decades, with advanced data collection techniques, a different type of data continues to appear in various biological, sciences, medical, social, and economical studies. Statistical modeling is essential in many scientific research areas because it explains the relationship between the response variable of interest and a number of explanatory variables. Generalized linear models (GLMs) are generalization of the linear regression models, which allow fitting regression models to response variable that is non normal and follows a general exponential family. The aim of this study is to encourage and initiate the application of GLMs to predict the maternal and fetal blood-lead level. The inverse Gaussian distribution with inverse quadratic link function is considered. Four main effects were significant in the prediction of the maternal blood-lead level (pica, smoking of mother, dairy products intake of mother, calcium intake of mother), while in the prediction of the fetal blood-lead level, two main effects showed significance (dairy products intake of mother and hemoglobin of mother). Keywords: Generalized Linear Models; Exponential Family; Inverse Gaussian distribution; Link Functions.

2017-05

IOSR Journal of Mathematics (Issue : 3) (Volume : 13)

Gamma regression is a member of generalized liner models and often used when the phenomenon under study is skewed and the mean is proportional to the standard deviation. It can find applications in several areas such as life-testing problems, forecasting cancer incidences, weather extremes and quality control. Also it is a natural candidate when modeling the variance and it has been increasingly used over the past decade. paper attempts to introduce readers with the concept of the gamma regression model, in which the dependent variable has the gamma distribution, and the use of the paired bootstrapping resampling associated with the "boot" package in R program. Three confidence intervals were computed.

2017-05

IOSR Journal of Mathematics (Issue : 3) (Volume : 13)

Decades after its discussion in (Koenker and Bassett, 1978), quantile regression (QR) has been the topic of great practical applications in many areas: economics, ecology, biology and so on. In this paper, we present Bayesian quantile regression using two level prior distributions. Specifically, we assume that the prior distribution of each regression coefficient is a zero mean normal prior distribution with unknown variance. Then, we assign noninformative Jeffreys prior distributions for the variances assuming they are independent. A Gibbs sampler algorithm is developed for the posterior inference. The new method is illustrated via simulations and a real dataset.

2017-05

IOSR Journal of Mathematics (Issue : 3) (Volume : 13)

Robust regression is an alternative to the least squares method that can be appropriately used when there is evidence that the distribution of the error term is non-normal (heavy-tailed) and/or there are outliers that affect the regression equation. The least squares method has been in use in regression analysis mainly because of tradition and ease of computation, but this method may suffer a huge setback in the presence of unusual observation such as outliers and high leverage point. In this paper our main objective was to use jackknife after bootstrap procedure in most of robust regression method like, M-estimator and MM-estimator. Analytical examples are presented to show the effective of the deleted observation on the coefficients, and the behavior of jackknife after bootstrap in robust regression.

2017-03

المجلة الاكاديمية لجامعة نوروز (Issue : 6) (Volume : 1)

لقد تم استخدام بيانات السلسة الزمنية لدراسة وتحليل البياانت الشهرية عن معدل الطاقة الكهربائية المجهزة الى مدينة دهوك للفترة (2000-2013) لتقديرالنماذج وابقيت الاثني عشر الاخيرة التي تمثل عام 2014 لاغراض المقارنة مع التنبؤات التي يتم الحصول عليها

2010-12

المجلة العراقية للعلوم الاحصائية (Issue : 2010) (Volume : 18)

This paper presents fits for neural network model , and comparative resulting forecasts with those obtained from BoxJenkins Method. We use time series data of Tigris's monthly flow into Mosul city from 1950-1995. To perform a comparative . forecasting work through the Box-Jenkins and neural network doesn't mean working with two different or competing aspect ; on the contrary choosing a proper architecture of neural net works requires using the skills of statistical modeling . As for application , Box-Jenkins Method has given more appropriate forecasts than those given by feed forward artificial neural network . We used Minitab and SPSS programs in the statistical aspect and Alyuda program in the neural network aspect.