Empirical likelihood estimation of the spatial quantile regression

The spatial quantile regression model is a useful and flexible model for analysis of empirical problems with spatial dimension. This paper introduces an alternative estimator for this model. The properties of the proposed estimator are discussed in a comparative perspective with regard to the other available estimators. Simulation evidence on the small sample properties of the proposed estimator is provided. The proposed estimator is feasible and preferable when the model contains multiple spatial weighting matrices. Furthermore, a version of the proposed estimator based on the exponentially tilted empirical likelihood could be beneficial if model misspecification is suspect

Disease Mapping via Negative Binomial Regression M-quantiles

We introduce a semi-parametric approach to ecological regression for disease mapping, based on modelling the regression M-quantiles of a Negative Binomial variable. The proposed method is robust to outliers in the model covariates, including those due to measurement error, and can account for both spatial heterogeneity and spatial clustering. A simulation experiment based on the well-known Scottish lip cancer data set is used to compare the M-quantile modelling approach and a random effects modelling approach for disease mapping. This suggests that the M-quantile approach leads to predicted relative risks with smaller root mean square error than standard disease mapping methods. The paper concludes with an illustrative application of the M-quantile approach, mapping low birth weight incidence data for English Local Authority Districts for the years 2005-2010.Comment: 23 pages, 7 figure

Spatial Quantile Regression

In a number of applications, a crucial problem consists in describing and analyzing the influence of a vector Xi of covariates on some real-valued response variable Yi. In the present context, where the observations are made over a collection of sites, this study is more difficult, due to the complexity of the possible spatial dependence among the various sites. In this paper, instead of spatial mean regression, we thus consider the spatial quantile regression functions. Quantile regression has been considered in a spatial context. The main aim of this paper is to incorporate quantile regression and spatial econometric modeling. Substantial variation exists across quantiles, suggesting that ordinary regression is insufficient on its own. Quantile estimates of a spatial-lag model show considerable spatial dependence in the different parts of the distribution.W wielu zastosowaniach, podstawowym problemem jest opis i analiza wpływu wektora skorelowanych zmiennych objaśniających X na zmienna objaśnianą Y. W przypadku, gdy obserwacje badanych zmiennych są dodatkowo rozmieszczone przestrzennie, zadanie jest jeszcze trudniejsze, ponieważ mamy dodatkowe zależności, wynikające ze zmienności przestrzennej. W tej pracy, w miejsce przestrzennej regresji wykorzystującej średnią, rozpatrzymy przestrzenna regresję kwantylową. Regresja kwantylowa zostanie omówiona w przestrzennym kontekście. Głównym celem pracy jest wskazanie na możliwości powiązania metodologii regresji kwantylowej i ekonometrycznego modelowania przestrzennego. Dodatkowe zasoby informacji o zmienności otrzymujemy badając kwantyle, wychodząc poza tradycyjny opis klasycznej regresji. Estymacja kwantylowa w modelu przestrzennym uwydatnia zależności przestrzenne dla różnych fragmentów rozważanych rozkładów

Search Activities, Cost of Living and Local Labor Markets

A model is considered in which optimal search intensity is a result of a trade off between short run losses due to higher search costs (more interviews, commuting...) and long-run gains due to a higher chance of finding a job. We show that this optimal search intensity is higher in areas characterized by larger cost of living and/or higher labor market tightness. This model is then tested for England on sub-regional data. We estimate a spatial error model and we find that both the local cost of living and the local labor market tightness are found to have a positive and significant effect on unemployed average search intensity. These findings are consistent with the prediction of the theoretical model.Job Matching; Search Intensities; Spatial Correlation

Convergence in per-capita GDP across European regions using panel data models extended to spatial autocorrelation effects.

This paper studies the convergence of per capita GDP across European regions over a fairly long period. Most of the works are based on either cross-sectional or fixed-effects estimates. We propose the estimation of convergence in per capita GDP across European regions by making use of panel-data models extended to include spatial error autocorrelation and spatially lagged dependent variable (Anselin,1988;Elhorst,2002). This will allow us to extend the traditional ß convergence model to include a rigorous treatment of the spatial correlation among the intercept terms. A spatial analysis of such intercept terms will also be performed in order to shed light on the concept spatially conditional convergence.