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.

Does the International Tourism Industry Relax Sovereign Credit Ratings: The Case of Countries Most Reliant on Tourism

Sustainable tourism plays a dominant role in the economic well-being of some of the world’s countries, especially small ones. Tourism earnings account for a significant proportion of their GDP, and they have an overwhelming reliance on tourism as a source of service exports. The general trends in tourism earnings and volatilities in country risk ratings often go hand in hand, especially for small touristic countries in that region. The research presented in this paper provides a comparative assessment of the international country risk ratings and highlights the importance of their tourism earnings and tourism export. This study employs the ordered response and Poisson count panel data model for a sample of twenty-two countries most reliant on tourism, including Mediterranean countries. The aim of this study is to investigate whether the tourism determinants of sovereign credit ratings for those countries vary between different rating agencies (Standard & Poor’s, Moody’s and Fitch’s). The key finding is that an increase in tourism earnings as a proportion of GDP and as the main export share in the total country export impairs the sovereign risk rating and turns out to be robust across the different methodologies.

MODELLING AND ESTIMATING INDIVIDUAL AND FIRM EFFECTS WITH COUNT PANEL DATA

We propose a new parametric model for the modelling and estimation of event distributions for individuals in different firms. The analysis uses panel data and takes into account individual and firm effects in a non-linear model. Non-observable factors are treated as random effects. In our application, the distribution of accidents is affected by observable and non-observable factors from vehicles, drivers and fleets of vehicles. Observable and unobservable factors are significant to explain road accidents, which mean that insurance pricing should take into account all these factors. A fixed effects model is also estimated to test the consistency of the random effects model.