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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.

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.

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 A. I. Abd-Elftah, "Forecasting Egyptian GDP Using ARIMA Models", Reports on Economics and Finance, vol. 5, issue 1, pp. 35 - 47, 2019. Abstractforecasting_egyptian_gdp_using_arima_models.pdf

The Gross Domestic Product (GDP) is that the value of all products and services made at intervals the borders of a nation in an exceedingly year. In this paper, the Box-Jenkins approach has been used to build the appropriate Autoregressive-Integrated Moving-Average (ARIMA) model for the Egyptian GDP data. Egypt’s annual GDP data obtained from the World-Bank for the years 1965 to 2016. We find that the appropriate statistical model for Egyptian GDP is ARIMA (1, 2, 1). Finally, we used the fitted ARIMA model to forecast the GDP of Egypt for the next ten years.

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. 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.

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.

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.

Abonazel, M. R., "Advanced Statistical Techniques Using R: Outliers and Missing Data", Annual Conference on Statistics, Computer Sciences and Operations Research, Faculty of Graduate Studies for Statistical Research, Cairo University, 2019. AbstractAdvanced_statistical_techniques_using_r_outliers_and_missing_data.pdf

This paper has reviewed two important problems in regression analysis (outliers and missing data), as well as some handling methods for these problems using R. Moreover, two R-applications have been introduced to understand these methods by R-codes. Finally, we created a simple simulation study to compare different handling methods of missing data; this is an example of how to create R-codes to perform Monte Carlo simulation studies.

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., 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.

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.

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., 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., "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., "Handling Outliers and Missing Data in Regression Models Using R: Simulation Examples", Academic Journal of Applied Mathematical Sciences, vol. 6, issue 8, pp. 187-203, 2020. AbstractHandling outliers and missing data using R simulation examples.pdfWebsite

This paper has reviewed two important problems in regression analysis (outliers and missing data), as well as some handling methods for these problems. Moreover, two applications have been introduced to understand and study these
methods by R-codes. Practical evidence was provided to researchers to deal with those problems in regression modeling
with R. Finally, we created a Monte Carlo simulation study to compare different handling methods of missing data in the
regression model. Simulation results indicate that, under our simulation factors, the k-nearest neighbors method is the
best method to estimate the missing values in regression models.

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.

Abonazel, M. R., "Generalized estimators of stationary random-coefficients panel data models: asymptotic and small sample properties", REVSTAT – Statistical Journal, vol. 17, issue 4, pp. 493–521, 2019. Abstractgeneralized_estimators_of_stationary.pdf

This article provides generalized estimators for the random-coefficients panel data (RCPD) 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, under the new assumptions of the error, the conventional estimators are not suitable for RCPD model. Therefore, the suitable estimator for this model and other alternative estimators have been provided and examined in this article. Furthermore, 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 efficient than the conventional estimators, especially in small samples.

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 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.

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., 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.

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.

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