The aim of this work is to test empirically the validity of Gibrat's Law in the growth of cities, using data for all the twentieth century of the complete distribution of cities (without any size restrictions) in three countries: the US, Spain and Italy. For this we use different techniques...

Top distributions of income and wealth are still incompletely measured in many national statistics, particularly when using survey data. This paper develops the technique of incorporating the joint distributional relationship to enhance the estimation of these two top distributions. We leverage...

In this paper we provide a brief overview of some of the most recent empirical research on spatial econometric models and spatial data mining. Data mining in general is the search for hidden patterns that may exist in large databases. Spatial data mining is a process to discover interesting,...

Accumulation of education and geographic concentration of educated people in cities are expected to generate urban income growth. New economic geography predicts income divergence across regions. We investigate the dynamic process of accumulating tertiary education and regional income growth in...

It is quite common in convergence analyses across regions that data exhibit strong spatial dependence. While the literature adopting the regression approach is now fully aware that neglecting this feature may lead to inaccurate results and has therefore suggested a number of statistical tools...

This paper deals with the relationship between knowledge, innovation and regional growth. The study is carried out through the application of nonparametric estimation methods to European data at NUTS2 level. We provide evidence that the share of innovative ...firms plays a more relevant role in...

Nonlinear estimation of the gravity model with Poisson/negative binomial methods has become popular to model international trade flows, as it permits a better accounting for large numbers of zero flows. Nevertheless, as trade flows are not independent of each other due to spatial...

We propose a geoadditive negative binomial model (Geo-NB-GAM) for regional count data which allows us to simultaneously address some important methodological issues, such as spatial clustering, nonlinearities and overdispersion. We apply this model to study location determinants of inward...