Publications

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2019
Abonazel, M. R., N. Helmy, and A. Azazy, "The Performance of Speckman Estimation for Partially Linear Model using Kernel and Spline Smoothing Approaches", International Journal of Mathematical Archive, vol. 10, issue 6, pp. 10-18, 2019. Abstractthe_performance_of_speckman_estimation.pdf

The Speckman method is a commonly used for estimating the partially linear model (PLM). This method used the
kernel approach to estimate nonparametric part in PLM. In this paper, we suggest using the spline approach instead of the kernel approach. Then we present a comparative study of the two estimations based on two smoothing (kernel and spline) approaches. A simulation study has been conducted to evaluate the performance of these estimations. The results of our study confirmed that the spline smoothing approach was the best.

2018
Abonazel, M. R., "A Practical Guide for Creating Monte Carlo Simulation Studies Using R", International Journal of Mathematics and Computational Science, vol. 4, issue 1, pp. 18-33, 2018. AbstractMonte Carlo simulation studies using R.pdf

This paper considers making Monte Carlo simulation studies using R language. Monte Carlo simulation techniques are very
commonly used in many statistical and econometric studies by many researchers. So, we propose a new algorithm that
provides researchers with basics and advanced skills about how to create their R-codes and then achieve their simulation
studies. Our algorithm is a general and suitable for creating any simulation study in statistical and econometric models.
Moreover, we provide some empirical examples in econometrics as applications on this algorithm.

Abonazel, M. R., "Different Estimators for Stochastic Parameter Panel Data Models with Serially Correlated Errors", Journal of Statistics Applications and Probability, vol. 7, issue 3, pp. 423-434, 2018. Abstractdifferent_estimators_for_stochastic_parameter_panel_data.pdfWebsite

This paper considers stochastic parameter panel data models when the errors are first-order serially correlated. The feasible generalized least squares (FGLS) and simple mean group (SMG) estimators for these models have been reviewed and examined. The efficiency comparisons for these estimators have been carried when the regression parameters are stochastic, non-stochastic, and mixed stochastic. Monte Carlo simulation study and a real data application are given to evaluate the performance of FGLS and SMG estimators. The results indicate that, in small samples, SMG estimator is more reliable in most situations than FGLS estimators, especially when the model includes one or more non-stochastic parameter.

Abonazel, M. R., "Efficiency Comparisons of Different Estimators for Panel Data Models with Serially Correlated Errors: A Stochastic Parameter Regression Approach", International Journal of Systems Science and Applied Mathematics, vol. 3, no. 2: Science Publishing Group, pp. 37, 2018. Abstract
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Abonazel, M. R., and M. G. Ibrahim, "On Estimation Methods for Binary Logistic Regression Model with Missing Values", International Journal of Mathematics and Computational Science, vol. 4, issue 3, pp. 79-85, 2018. Abstract

This paper reviews some estimation methods for the binary logistic regression model with missing data in dependent and/or independent variables. Moreover, we present an empirical study for assessing the performance of these estimation methods under the existence of missing data. The results indicated that the regression imputation method is a very appropriate method for estimating the missing values in this model.

2017
Youssef, A. H., and M. R. Abonazel, "Alternative GMM Estimators for First-order Autoregressive Panel Model: An Improving Efficiency Approach", Communications in Statistics - Simulation and Computation, vol. 46, issue 4, no. ja, pp. 3112-3128, 2017. AbstractWebsite

This paper considers first-order autoregressive panel model which is a
simple model for dynamic panel data (DPD) models. The generalized
method of moments (GMM) gives efficient estimators for these models.
This efficiency is affected by the choice of the weighting matrix which has
been used in GMM estimation. The non-optimal weighting matrices have
been used in the conventional GMM estimators. This led to a loss of
efficiency. Therefore, we present new GMM estimators based on optimal or
suboptimal weighting matrices. Monte Carlo study indicates that the bias
and efficiency of the new estimators are more reliable than the
conventional estimators.

Abonazel, M. R., "Bias correction methods for dynamic panel data models with fixed effects", International Journal of Applied Mathematical Research, vol. 6, issue 2, pp. 58-66, 2017. Abstractijamr-7774.pdf

This paper considers the estimation methods for dynamic panel data (DPD) models with fixed effects which suggested in econometric literature, such as least squares (LS) and generalized method of moments (GMM). These methods obtain biased estimators for DPD models. The LS estimator is inconsistent when the time dimension (T) is short regardless of the cross sectional dimension (N). Although consistent estimates can be obtained by GMM procedures, the inconsistent LS estimator has a relatively low variance and hence can lead to an estimator with lower root mean square error after the bias is removed. Therefore, we discuss in this paper the different methods to correct the bias of LS and GMM estimations. The analytical expressions for the asymptotic biases of the LS and GMM estimators have been presented for large N and finite T. Finally, we display new estimators that presented by Youssef and Abonazel [40] as more efficient estimators than the con-ventional estimators.

El-sheikh, A. A., M. R. Abonazel, and N. Gami, "A Review of Software Packages for Structural Equation Modeling: A Comparative Study", Applied Mathematics and Physics, vol. 5, issue 3, pp. 85-94, 2017. Abstracta_review_of_software_packages_for_structural_equation_modeling_a_comparative_study.pdf

Structural equation modeling (SEM) is a widely used statistical method in most of social science fields. Similar to other statistical methods, the choice of the appropriate estimation methods affects the results of the analysis, thus it was of importance to review some of SEM software packages and the availability of different estimation methods in these packages. Therefore, in this paper five SEM software packages (AMOS, LISREL, and three packages in R) dealing with SEM analysis were reviewed to guide the researcher about the usage of each package. Moreover, an empirical study was presented to assess the performance of different estimation methods under the existence of missing data. The results showed that full information maximum likelihood (FIML) was the best estimation method to deal with different missingness rates.

Abonazel, M. R., and M. H. M., "Using Completely Randomized Design of Parallel Linear Model for Estimating the Biological Potency of Human Insulin Drugs: An Empirical Study", Biostatistics and Biometrics Open Access Journal, vol. 3, issue 4, pp. 1-7, 2017. Abstractbboaj.ms_.id_.555619.pdfWebsite

In this article, we propose using the completely randomized design of parallel linear model in the statistical analysis of the biological assay of Human Insulin injection used by patients with Diabetes mellitus type II. Check the efficacy of insulin drugs should take place because either lower or higher efficacy from the acceptable limits has its complication. Four different batches of insulin product were analyzed by using human insulin reference standard. Estimating the biological activity as a relative potency (relative to the standard) of each batch the result of the four batches was within the acceptable limit of the drug specification. Moreover, we compare our bioassay results with the chemical assay (using HPLC) results of the same batches. Then, we found that all the results were within the acceptable limits and led us to the same conclusions.

2016
Abonazel, M. R., "Generalized Random Coefficient Estimators of Panel Data Models: Asymptotic and Small Sample Properties", American Journal of Applied Mathematics and Statistics, vol. 4, no. 2, pp. 46–58, 2016. AbstractWebsite

This paper provides a generalized model for the random-coefficients panel data model where the errors are cross-sectional heteroskedastic and contemporaneously correlated as well as with the first-order autocorrelation of the time series errors. Of course, the conventional estimators, which used in standard random-coefficients panel data model, are not suitable for the generalized model. Therefore, the suitable estimator for this model and other alternative estimators have been provided and examined in this paper. Moreover, the efficiency comparisons for these estimators have been carried out in small samples and also we examine the asymptotic distributions of them. The Monte Carlo simulation study indicates that the new estimators are more reliable (more efficient) than the conventional estimators in small samples.

2015
Abonazel, M. R., "How to Create a Monte Carlo Simulation Study using R: with Applications on Econometric Models", Annual Conference on Statistics, Computer Sciences and Operations Research, Egypt, 30 DECEMBER , 2015. Abstracthow_to_create_a_monte_carlo_simulation_study_using_r_with_applications_on_econometric_models.pdf

In this workshop, we provide the main steps for making the Monte Carlo simulation study using R language. A Monte Carlo simulation is very common used in many statistical and econometric studies by many researchers. We will extend these researchers with the basic information about how to create their R-codes in an easy way. Moreover, this workshop provides some empirical examples in econometrics as applications. Finally, the simple guide for creating any simulation R-code has been produced.

2014
Abonazel, M. R., "Statistical Analysis using R", Annual Conference on Statistics, Computer Sciences and Operations Research, Egypt , 2014. Abstract

This presentation for a workshop about the basics of R language and use it for data analysis.

Youssef, A. H., A. A. Elshekh, and M. R. Abonazel, "Improving the Efficiency of GMM Estimators for Dynamic Panel Models", Far East Journal of Theoretical Statistics, vol. 47, issue 2, pp. 171-189, 2014. Abstractimprove_the_efficiency_of_gmm_estimators_for_dynamic_panel_models.pdfWebsite

In dynamic panel models, the generalized method of moments (GMM) has been used in many applications since it gives efficient estimators. This efficiency is affected by the choice of the initial weighted matrix. It is common practice to use the inverse of the moment matrix of the instruments as an initial weighted matrix. However, an initial optimal weighted matrix is not known, especially in the system GMM estimation procedure. Therefore, we present the optimal weighted matrix for level GMM estimator, and suboptimal weighted matrices for system GMM estimator, and use these matrices to increase the efficiency of GMM estimator. By using the Kantorovich inequality (KI), we find that the potential efficiency gain becomes large when the variance of individual effects increases compared with the variance of the errors.

Youssef, A. H., A. A. El-sheikh, and M. R. Abonazel, "New GMM Estimators for Dynamic Panel Data Models", International Journal of Innovative Research in Science, Engineering and Technology, vol. 3, issue 10, pp. 16414-16425, 2014. Abstractnew_gmm_estimators_for_dynamic_panel_data_models.pdf

In dynamic panel data (DPD) models, the generalized method of moments (GMM) estimation gives efficient estimators. However, this efficiency is affected by the choice of the initial weighting matrix. In practice, the inverse of the moment matrix of the instruments has been used as an initial weighting matrix which led to a loss of efficiency. Therefore, we will present new GMM estimators based on optimal or suboptimal weighting matrices in GMM estimation. Monte Carlo study indicates that the potential efficiency gain by using these matrices. Moreover, the bias and efficiency of the new GMM estimators are more reliable than any other conventional GMM estimators.

Abonazel, M. R., Some Estimation Methods for Dynamic Panel Data Models, , Cairo, Cairo University , 2014. Abstractsummary_of_phd_mohamed_r._abonazel.pdf

This thesis considers estimation of dynamic panel data models under different assumptions, and we focus on explore the bias properties of the different estimation methods. And, we focus on GMM estimation because of it has been used in many applications and it gives efficient estimators. This efficiency is affected by the choice of the initial weighting matrix. It is common practice to use the inverse of the moment matrix of the instruments as an initial weighting matrix. However, an initial optimal weighting matrix is not known, especially in the system GMM estimation procedure.Therefore, the main objective of this thesis is to improve the efficiency of GMM estimators.
To achieve this objective we proposed new approach to improve the efficiency of GMM estimators. Our approach based on finding and using the optimal weighting matrices to obtain more efficient estimators.

2011
Mousa, A., A. H. Youssef, and M. R. Abonazel, "A Monte Carlo Study for Swamy’s Estimate of Random Coefficient Panel Data Model", InterStat Journal , vol. 2011, issue April, No. 4, pp. 1-12, 2011. Abstracta_monte_carlo_study_for_swamys_estimate_of_random_coefficient_panel_data_model_2011.pdfWebsite

A particularly useful approach for analyzing pooled cross sectional and time series data is Swamy's random coefficient panel data (RCPD) model. This paper examines the performance of Swamy's estimators and tests associated with this model by using Monte Carlo simulation. The Monte Carlo study shed some light into how well the Swamy's estimate perform in small, medium, and large samples, in cases when the regression coefficients are fixed, random, and mixed. The Monte Carlo simulation results suggest that the Swamy's estimate perform well in small samples if the coefficients are random and but it does not when regression coefficients are fixed or mixed. But if the samples sizes are medium or large, the Swamy's estimate performs well when the regression coefficients are fixed, random, or mixed.

2009
Youssef, A. H., and M. R. Abonazel, "A Comparative Study for Estimation Parameters in Panel Data Model", InterStat Journal , vol. 2009, issue May,No. 2, pp. 1-17, 2009. Abstracta_comparative_study_for_estimation_parameters_in_panel_data_model__2009.pdfWebsite

This paper examines the panel data models when the regression coefficients are fixed, random, and mixed, and proposed the different estimators for this model. We used the Mote Carlo simulation for making comparisons between the behavior of several estimation methods, such as Random Coefficient Regression (RCR), Classical Pooling (CP), and Mean Group (MG) estimators, in the three cases for regression coefficients. The Monte Carlo simulation results suggest that the RCR estimators perform well in small samples if the coefficients are random. While CP estimators perform well in the case of fixed model only. But the MG estimators perform well if the coefficients are random or fixed.