The performance of count panel data estimators: a simulation study and application to patents in Arab countries

Abstract

This paper provides four estimators of count panel data (CPD) models; fixed effects Poisson (FEP), random effects Poisson (REP), fixed effects negative binomial (FENB), and random effects negative binomial (RENB). In FEP and FENB models, we used conditional maximum likelihood (CML) estimation method. While for REP and RENB models, we used maximum likelihood (ML) estimation method. We conducted a Monte Carlo simulation study to compare the behavior of these estimators in the four models. The results of simulation show that the best estimator is FENB compared to other estimators (FEP, REP, and RENB), because it has minimum values for Akaike information criterion (AIC) and Bayesian information criterion (BIC), especially when the model or the data has an overdispersion problem. Moreover, a real dataset has been used to study the effect of some economic variables on the number of patents for seven Arab countries over the period from 2000 to 2016. Application results indicate that the RENB is the suitable model for this data, and the important (statistically significant) variables that effect on the number of patents is the gross domestic product per capita.