Location Determinants of Greenfield Foreign Investments in the Enlarged Europe: Evidence from a Spatial Autoregressive Negative Binomial Additive Model

This paper addresses two important methodological issues in the analysis of industrial location: spatial dependence and nonlinearities. To this end, we estimate a semi-parametric spatial autoregressive negative binomial model using data on the number of inward greenfield FDI occurred over the 2003-2007 period in 249 European regions. Results support the view that multinational firms’ location choices are very spatially dependent, even controlling for a large number of regional characteristics. A spatial lag model with a non-parametric spatial filter allows us to purge the residuals from spatial dependence and yields sensible changes in the magnitude of some estimated coefficients. We also provide robust evidence of nonlinearities. In particular, we find that the effect of agglomeration economies fades down as the density of economic activities reaches some limit value.Multinational firms, greenfield FDI, count data, spatial econometrics, semiparametric econometrics

Disease mapping and regression with count data in the presence of overdispersion and spatial autocorrelation: a Bayesian model averaging approach

This paper applies the generalised linear model for modelling geographical variation to esophageal cancer incidence data in the Caspian region of Iran. The data have a complex and hierarchical structure that makes them suitable for hierarchical analysis using Bayesian techniques, but with care required to deal with problems arising from counts of events observed in small geographical areas when overdispersion and residual spatial autocorrelation are present. These considerations lead to nine regression models derived from using three probability distributions for count data: Poisson, generalised Poisson and negative binomial, and three different autocorrelation structures. We employ the framework of Bayesian variable selection and a Gibbs sampling based technique to identify significant cancer risk factors. The framework deals with situations where the number of possible models based on different combinations of candidate explanatory variables is large enough such that calculation of posterior probabilities for all models is difficult or infeasible. The evidence from applying the modelling methodology suggests that modelling strategies based on the use of generalised Poisson and negative binomial with spatial autocorrelation work well and provide a robust basis for inference

How robust are the estimated effects of air pollution on health? Accounting for model uncertainty using Bayesian model averaging

The long-term impact of air pollution on human health can be estimated from small-area ecological studies in which the health outcome is regressed against air pollution concentrations and other covariates, such as socio-economic deprivation. Socio-economic deprivation is multi-factorial and difficult to measure, and includes aspects of income, education, and housing as well as others. However, these variables are potentially highly correlated, meaning one can either create an overall deprivation index, or use the individual characteristics, which can result in a variety of pollution-health effects. Other aspects of model choice may affect the pollution-health estimate, such as the estimation of pollution, and spatial autocorrelation model. Therefore, we propose a Bayesian model averaging approach to combine the results from multiple statistical models to produce a more robust representation of the overall pollution-health effect. We investigate the relationship between nitrogen dioxide concentrations and cardio-respiratory mortality in West Central Scotland between 2006 and 2012

Advocating better habitat use and selection models in bird ecology

Studies on habitat use and habitat selection represent a basic aspect of bird ecology, due to its importance in natural history, distribution, response to environmental changes, management and conservation. Basically, a statistical model that identifies environmental variables linked to a species presence is searched for. In this sense, there is a wide array of analytical methods that identify important explanatory variables within a model, with higher explanatory and predictive power than classical regression approaches. However, some of these powerful models are not widespread in ornithological studies, partly because of their complex theory, and in some cases, difficulties on their implementation and interpretation. Here, I describe generalized linear models and other five statistical models for the analysis of bird habitat use and selection outperforming classical approaches: generalized additive models, mixed effects models, occupancy models, binomial N-mixture models and decision trees (classification and regression trees, bagging, random forests and boosting). Each of these models has its benefits and drawbacks, but major advantages include dealing with non-normal distributions (presence-absence and abundance data typically found in habitat use and selection studies), heterogeneous variances, non-linear and complex relationships among variables, lack of statistical independence and imperfect detection. To aid ornithologists in making use of the methods described, a readable description of each method is provided, as well as a flowchart along with some recommendations to help them decide the most appropriate analysis. The use of these models in ornithological studies is encouraged, given their huge potential as statistical tools in bird ecology.Fil: Palacio, Facundo Xavier. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Naturales y Museo. División Zoología de Vertebrados. Sección Ornitología; Argentin

Smooth-car mixed models for spatial count data

Penalized splines (P-splines) and individual random effects are used for the analysis of spatial count data. P-splines are represented as mixed models to give a unified approach to the model estimation procedure. First, a model where the spatial variation is modelled by a two-dimensional P-spline at the centroids of the areas or regions is considered. In addition, individual area-effects are incorporated as random effects to account for individual variation among regions. Finally, the model is extended by considering a conditional autoregressive (CAR) structure for the random effects, these are the so called “Smooth-CAR” models, with the aim of separating the large-scale geographical trend, and local spatial correlation. The methodology proposed is applied to the analysis of lip cancer incidence rates in Scotland.Mixed models, P-splines, Overdispersion, Negative Binomial, PQL, CAR models, Scottish lip cancer data

Smooth-car mixed models for spatial count data

Penalized splines (P-splines) and individual random effects are used for the analysis of spatial count data. P-splines are represented as mixed models to give a unified approach to the model estimation procedure. First, a model where the spatial variation is modelled by a two-dimensional P-spline at the centroids of the areas or regions is considered. In addition, individual area-effects are incorporated as random effects to account for individual variation among regions. Finally, the model is extended by considering a conditional autoregressive (CAR) structure for the random effects, these are the so called “Smooth-CAR” models, with the aim of separating the large-scale geographical trend, and local spatial correlation. The methodology proposed is applied to the analysis of lip cancer incidence rates in Scotland

Model selection and sensitivity analysis in a class of Bayesian spatial distribution models

This work is focused on Bayesian hierarchical modeling of geographical distribution of marine species Coregonus lavaretus L. s.l. along the Gulf of Bothnia. Spatial dependences are modeled by Gaussian processes. The main modeling objective is to predict whitefish larvae distribution for previously unobserved spatial locations along the Gulf of Bothnia. In order to achieve this objective, we have to solve two main tasks: to investigate the sensitivity of posterior parameters estimates with respect to different parameter priors, and to solve model selection task. In model selection, among all candidate models, we have to choose the model with best predictive performance. The candidate models were divided into two main groups: models that describe spatial effects, and models without such description. The candidates in each group involved different number (6 or 8) and expressions of environmental variables. In the group describing spatial effects, we analyzed four different models of Gaussian mean, and for every mean model we used four different prior parameters combinations. The same four models of latent function were used in the candidates where spatial dependences were not described. For every such model we assigned four different priors of overdispersion parameter. Thus, all at all, 32 candidate models were analyzed. All candidate models were estimated with Hamiltonian Monte Carlo MCMC algorithm. Model checks were conducted using the posterior predictive distributions. The predictive distributions were evaluated using the logarithmic score with 10 fold cross validation. The analysis of posterior estimates in models describing spatial effects revealed, that these estimates were very sensitive to prior parameters choices. The provided sensitivity analysis helped us to choose the most suitable priors combination. The results from model selection showed that the model, which showed best predictive performance, does not need to be very complicated and to involve description of spatial effects when the data are not informative enough to detect well the spatial effects. Although the selected model was simpler, the corresponding predictive maps of log larvae intensity correctly predicted the larvae distribution along the Gulf of Bothnia