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A comparison of artificial neural network and time series models for forecasting GDP in Palestine
Safi, Samir K. (2016)Time series of quarterly observations on Gross Domestic Product (GDP) is collected and used in this study. Forecasting results of ANNs are compared with those of the Autoregressive Integrated Moving Average (ARIMA) and ... 
An Introduction to Analysis of Regression Models by EViews (Part 1)
Safi, Samir K. (20140208)An Introduction to Analysis of Regression Models by EViews 
An Introduction to Analysis of Regression Models by EViews (Part 2)
Safi, Samir K. (20140208)An Introduction to Analysis of Regression Models by EViews 
Artificial Neural Networks Approach to Time Series Forecasting for Electricity Consumption in Gaza Strip
Safi, Samir K. (الجامعة الإسلامية  غزة, 2013)This paper introduces two robust forecasting models for efficient forecasting, Artificial Neural Networks (ANNs) approach and Autoregressive Integrated Moving Average (ARIMA) models. ANNs approach to univariate time series ... 
Attributes and characteristics of poor households in Gaza Strip  measurement indicators
Migdad, Mohammed; Elnamrouty, Khalil; Safi, Samir K. (2015) 
Building Enrolment Students' Forecasting Models in Faculty of Commerce at IUG in PalestineComparative Study
Safi, Samir K.Accuracy is an important issue in forecasting. As many factors have effects on college enrollment, researchers tend to add more and more variables in enrollment forecasting models. Does a more complex model necessarily do ... 
Building Logistic Regression Model to Identify Key Determinants of Poverty in Palestine
Safi, Samir K.; Elnamrouty, Khalil (2012)This paper aims to identify the key determinants of poverty which affect the poverty status of a household in Palestine since the implementation of the economic reform program. A logistic regression model will be used to ... 
Comparative study of portmanteau tests for the residuals autocorrelation in ARMA models
Safi, Samir K.; AlReqep, Alaa A. (Science Publishing Group, 2014)The portmanteau statistic for testing the adequacy of an autoregressive moving average (ARMA) model is based on the first m autocorrelations of the residuals from the fitted model. We consider some of portmanteau tests for ... 
Comparative study on forecasting accuracy among moving average models with simulation and PALTEL stock market data in Palestine
Safi, Samir K.; Dawoud, Issam A. (2013)In this paper, we discuss three analytical time series models for selecting the more effective with an accurate forecasting models, among others. We analytically modify the stochastic realization utilizing (i) kth moving ... 
Comparison of Estimators in Regression Models with AR(1) and AR(2) Disturbances: When is OLS Efficient?
Safi, Samir K.It is well known that the ordinary least squares (OLS) estimates in the regression model are efficient when the disturbances have mean zero, constant variance and are uncorrelated. In problems concerning time series, it ... 
Distributions of Generalized Order Statistics and Parameters Estimation of Pareto Distribution in Statistical Explicit Forms
Safi, Samir K.; Al Sheikh Ahmed, Rehab H. (الجامعة الإسلامية  غزة, 2014)Abstract : We study some distributions of generalized order statistics (GOS) for Pareto distribution. In particular, we have derived the joint probability density function (pdf) for GOS from Pareto distribution in statistical ... 
Explicit Equations for ACF in Autoregressive Processes In the Presence of Heteroscedasticity Disturbances
Safi, Samir K. (2011)The autocorrelation function, ACF, is an important guide to the properties of a time series. Explicit equations are derived for ACF in the presence of heteroscedasticity disturbances in pth order autoregressive, AR (p), ... 
Explicit equations for ACF in the presence of heteroscedasticity disturbances in firstorder autoregressive models, AR (1)
Safi, Samir K. (2009)The autocorrelation function, ACF, is an important guide to the properties of a time series. We derive explicit equations for ACF in the presence of heteroscedasticity disturbances in firstorder autoregressive, AR (1), ... 
Explicit Equations to Determine the Variances of Regression Coefficients of OLS and GLS Estimators In An AutoCorrelated Regression Models
Safi, Samir K. (الجامعة الإسلامية  غزة, 2008)We have derived explicit equations to determine the variances of the regression coefficients of ordinary least squares (OLS) and generalized least squares (GLS) estimators in regression models containing an autocorrelated ... 
Explicit Formulas to Determine the E¢ ciency of OLS in the Presence of First Order Autoregressive Disturbances
Safi, Samir K. (الجامعة الإسلامية  غزة, 2006)In problems concerning time series, it is often the case that the distur bances are, in fact, correlated. It is known that the ordinary least squares (OLS) may not be optimal in this context. We have proved that the rela ... 
FirstOrder Autoregressive Models, AR (1)
Safi, Samir K. (2009)The autocorrelation function, ACF, is an important guide to the properties of a time series. We derive explicit equations for ACF in the presence of heteroscedasticity disturbances in firstorder autoregressive, AR (1), ... 
Generalized Heteroskedasticity ACF for Moving Average Models in Explicit Forms
Safi, Samir K. (Pakistan Journal of Statistics and Operation Research, 2014)The autocorrelation function (ACF) measures the correlation between observations at different distances apart. We derive explicit equations for generalized heteroskedasticity ACF for moving average of order q, MA (q). We ... 
Generalized order statistics from generalized exponential distributions in explicit forms
Safi, Samir K.; Al Sheikh Ahmed, Rehab H. (2013)In the present paper, we study the generalized order statistics from Generalized Exponential Distributions (GED) in explicit forms. We obtain, the joint distribution, distribution of single, and conditional distribution ... 
On Distributions of Generalized Order Statistics from Kumaraswamy Distribution in Closed Forms
Safi, Samir K.The Kumaraswamy distribution which introduced by Poondi Kumaraswamy (1980) is similar to the Beta distribution but has the key advantage of a closedform cumulative distribution function. With its two nonnegative shape ...