Generalized Random Coefficient Estimators of Panel Data Models: Asymptotic and Small Sample Properties

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

Bias Correction Methods for Dynamic Panel Data Models with Fixed Effects

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 (2015) as more efficient estimators than the conventional estimators

How to Create a Monte Carlo Simulation Study using R: with Applications on Econometric Models

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

Bias Correction Methods for Dynamic Panel Data Models with Fixed Effects

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 (2015) as more efficient estimators than the conventional estimators

R-Codes to Calculate GMM Estimations for Dynamic Panel Data Models

These codes presented three functions for calculating three important estimators in dynamic panel data (DPD) models; these estimators are Arellano-Bond (1991), Arellano-Bover (1995), and Blundell-Bond (1998). All functions here need to the following variables: yit_1: dependent variable for DPD model; phi: the value of autoregressive coefficient; D.T_D.T: first-difference operator matrix of Arellano-Bond estimator; HD: instrumental variables of Arellano-Bond estimator; HL: instrumental variables of Arellano-Bover estimator; W: weighting matrix of Blundell-Bond estimator; HS: instrumental variables of Blundell-Bond estimator. Also, they need to the following R libraries: simex; plm; dlm. For more details about the theoretical bases and the developments of that estimators, see, e.g., Youssef et al. (2014a,b) and Youssef and Abonazel (2015). Moreover, these codes have been designed to enable the user to make a simulation study in this topic, such as the simulation study in Youssef et al. (2014b)

How to Create a Monte Carlo Simulation Study using R: with Applications on Econometric Models

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

Alternative GMM Estimators for First-order Autoregressive Panel Model: An Improving Efficiency Approach

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

Estimating the Number of Patents in the World Using Count Panel Data Models

In this paper, we review some estimators of count regression (Poisson and negative binomial) models in panel data modeling. These estimators based on the type of the panel data model (the model with fixed or random effects). Moreover, we study and compare the performance of these estimators based on a real dataset application. In our application, we study the effect of some economic variables on the number of patents for seventeen high-income countries in the world over the period from 2005 to 2016. The results indicate that the negative binomial model with fixed effects is the better and suitable for data, and the important (statistically significant) variables that effect on the number of patents in high-income countries are research and development (R&D) expenditures and gross domestic product (GDP) per capita

A Comparative Study for Estimation Parameters in Panel Data Model

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