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Abonazel, M., and N. Elnabawy, "Using the ARDL bound testing approach to study the inflation rate in Egypt", Economic consultant, vol. 31, issue 3, pp. 24-41, 2020. AbstractUsing the ARDL bound testing approach to study the inflation rate in Egypt

According to economic theory, the change in any economic variables may affect another economic variable through the time and these changes are not instantaneously, but also over future periods. The autoregressive distributed lag (ARDL) model has been used for decades to study the relationship between variables using a single equation time series. The ARDL model is one of the most general dynamic unrestricted models in econometric literature. In this model, the dependent variable is expressed by the lag and current values of independent variables and its own lag value.
This paper studies the dynamic causal relationships between inflation rate, foreign exchange rate, money supply, and gross domestic product (GDP) in Egypt during the period 2005: Q1 to 2018: Q2. Using the bounds testing approach to cointegration and error correction model, developed within an ARDL model, we investigate whether a long-run equilibrium relationship exists between the inflation rate and three determinants (foreign exchange rate, money supply, and GDP). The results indicate that the exchange rate and the growth in money supply have significant effects on the inflation rate in Egypt, while the real GDP has no significance effect on the inflation rate.

El-Masry, A. M., A. H. Youssef, and M. R. Abonazel, "Using logit panel data modeling to study important factors affecting delayed completion of adjuvant chemotherapy for breast cancer patients", Commun. Math. Biol. Neurosci., vol. 2021, pp. Article–ID, 2021. Abstract
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Abonazel, M. R., and O. A. Shalaby, "Using Dynamic Panel Data Modeling to Study Net FDI Inflows in MENA Countries", Studies in Economics and EconometricsStudies in Economics and Econometrics, vol. 44, issue 2: Routledge, pp. 1 - 28, 2020. AbstractWebsite

Foreign direct investment (FDI) plays a critical role in providing financial capital needs, technology transfer, and creating more jobs in the host country. It also helps economies to increase competitiveness and productivity, thereby increasing exports and enhancing opportunities for growth and development. Middle East and North Africa (MENA) countries are in desperate need of more FDI inflows to resolve their economic problems. This paper investigates the determinants of net FDI inflows to 23 countries in MENA region during the period from 1995 to 2017 by using static and dynamic panel data analysis. The results indicate that macro determinants, such as gross domestic product (GDP) growth rate, openness, the inflation rate, and public expenditure have a significant impact on net FDI inflows. In addition, we observe that rents from natural resource (oil), exchange rate, and total reserves of foreign exchange and monetary gold do not significantly influence FDI.

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.

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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.

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.

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Youssef, A. H., A. R. Kamel, and M. R. Abonazel, "Robust SURE estimates of profitability in the Egyptian insurance market", Statistical journal of the IAOS, vol. 37, no. 4: IOS Press, pp. 1275–1287, 2021. Abstract
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Abonazel, M. R., and A. A. - E. Gad, "Robust partial residuals estimation in semiparametric partially linear model", Communications in Statistics - Simulation and Computation, vol. 49, issue 5: Taylor & Francis, pp. 1223-1236, 2020. AbstractWebsite

This paper presents a robust version of partial residuals technique to estimate parametric and nonparametric components in semiparametric partially linear model. The robust estimation of the parametric component is constructed by using an M-estimation after eliminating the effect of the nonparametric component on both the response and covariates based on the pseudo data. Finally, the nonparametric component is estimated robustly by using the residuals from the obtained M-estimation of the parametric component. Simulation studies and a real data analysis illustrate that the proposed estimator performs better than the existing estimations when outliers in the dataset or errors with heavy tails.

Dawoud, I., and M. R. Abonazel, "Robust Dawoud–Kibria estimator for handling multicollinearity and outliers in the linear regression model", Journal of Statistical Computation and Simulation, vol. 91, no. 17: Taylor & Francis, pp. 3678–3692, 2021. Abstract
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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.

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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., 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.

Abonazel, M. R., S. M. El-sayed, and O. M. Saber, "Performance of robust count regression estimators in the case of overdispersion, zero inflated, and outliers: simulation study and application to German health data", Commun. Math. Biol. Neurosci., vol. 2021, pp. Article–ID, 2021. Abstract
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Abonazel, M. R., and O. Shalaby, "On Labor Productivity in OECD Countries: Panel Data Modeling", WSEAS TRANSACTIONS on BUSINESS and ECONOMICS, vol. 18, 2021. 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.

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Elgohary, M. M., M. R. Abonazel, N. M. Helmy, and A. R. Azazy, "New robust-ridge estimators for partially linear model", International Journal of Applied Mathematical Research, vol. 8, no. 2, pp. 46–52, 2019. Abstractnew_robust-ridge_estimators_for_partially_linear_model.pdf

This paper considers the partially linear model when the explanatory variables are highly correlated as well as the dataset contains outliers. We propose new robust biased estimators for this model under these conditions. The proposed estimators combine least trimmed squares and ridge estimations, based on the spline partial residuals technique. The performance of the proposed estimators and the Speckman-spline estimator has been examined by a Monte Carlo simulation study. The results indicated that the proposed estimators are more efficient and reliable than the Speckman-spline estimator.

Abonazel, M. R., "New Ridge Estimators of SUR Model When the Errors are Serially Correlated", International Journal of Mathematical Archive, vol. 10, issue 7, pp. 53-62, 2019. Abstractnew_ridge_estimators.pdf

This paper considers the seemingly unrelated regressions (SUR) model when the errors are first-order serially correlated as well as the explanatory variables are highly correlated. We proposed new ridge estimators for this model under these conditions. Moreover, the performance of the classical (Zellner’s and Parks’) estimators and the proposed (ridge) estimators has been examined by a Monte Carlo simulation study. The results indicated that the proposed estimators are efficient and reliable than the classical estimators.

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.

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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.

Lukman, A. F., B. Aladeitan, K. Ayinde, and M. R. Abonazel, "Modified ridge-type for the Poisson regression model: simulation and application", Journal of Applied Statistics: Taylor & Francis, pp. 1-13, 2021. AbstractWebsite

The Poisson regression model (PRM) is employed in modelling the relationship between a count variable (y) and one or more explanatory variables. The parameters of PRM are popularly estimated using the Poisson maximum likelihood estimator (PMLE). There is a tendency that the explanatory variables grow together, which results in the problem of multicollinearity. The variance of the PMLE becomes inflated in the presence of multicollinearity. The Poisson ridge regression (PRRE) and Liu estimator (PLE) have been suggested as an alternative to the PMLE. However, in this study, we propose a new estimator to estimate the regression coefficients for the PRM when multicollinearity is a challenge. We perform a simulation study under different specifications to assess the performance of the new estimator and the existing ones. The performance was evaluated using the scalar mean square error criterion and the mean squared error prediction error. The aircraft damage data was adopted for the application study and the estimators’ performance judged by the SMSE and the mean squared prediction error. The theoretical comparison shows that the proposed estimator outperforms other estimators. This is further supported by the simulation study and the application result.

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Abonazel, M. R., F. A. Awwad, A. F. Lukman, I. B. Lekara-Bayo, E. Y. Atanu, and others, "Long-run determinants of Nigerian inflation rate: ARDL bounds testing approach", WSEAS Transactions on Business and Economics, vol. 18: WSEAS, pp. 1370–1379, 2021. Abstract
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Abonazel, M. R., and R. A. Farghali, "Liu-Type Multinomial Logistic Estimator", Sankhya B, vol. 81, issue 2, pp. 203-225, Sep, 2019. AbstractWebsite

Multicollinearity in multinomial logistic regression affects negatively on the variance of the maximum likelihood estimator. That leads to inflated confidence intervals and theoretically important variables become insignificant in testing hypotheses. In this paper, Liu-type estimator is proposed that has smaller total mean squared error than the maximum likelihood estimator. The proposed estimator is a general estimator which includes other biased estimators such as Liu estimator and ridge estimator as special cases. Simulation studies and an application are given to evaluate the performance of our estimator. The results indicate that the proposed estimator is more efficient and reliable than the conventional estimators.

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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.

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