Skip to content
Research Article Open access CC BY 4.0

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

Ahmed H. Youssef, Mohamed R. Abonazel, Elsayed G. Ahmed

Asian Journal of Probability and Statistics · pp. 24–33 · Published 19 Mar 2020

10.9734/ajpas/2020/v6i430167

Abstract

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.

Conditional maximum likelihood estimation fixed effects model Hausman test negative binomial regression Poisson regression random effects model.

Cited by 16

Determinants of Per Capita Personal Income in the US: Spatial Fixed Effects Panel Data Modeling

A. H. Youssef, M. Abonazel, O. Shalaby · 2020

Proposed robust estimators for the Poisson panel regression model: application to COVID-19 deaths in Europe

Elsayed G. Ahmed, M. Abonazel, Mohammed Naji Al-Ghamdi · Communications in Mathematical Biology and Neuroscience · 2024

New robust estimators for the fixed effects negative binomial model: a simulation and real-world applications to European panel data

Coşkun Kuş, Zakariya Yahya, Algamal

Showing 13 of 16 known citations — external sources report more than can currently be individually listed.

Related research

Article metrics

Real usage data collected on this platform.

36

Page views

0

PDF downloads

1

Outbound clicks

16

Citations

Views over time

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

Traffic sources

Referring site, by host.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.