This paper proposes for the purposes of freight generation a spatial autoregressive model framework, combined with non-linear semi-parametric techniques. We demonstrate the capabilities of the model in a series of Monte Carlo studies. Moreover, evidence is provided for non-linearities in freight generation, through an applied analysis of European NUTS-2 regions. We provide evidence for significant spatial dependence and for significant non-linearities related to employment rates in manufacturing and infrastructure capabilities in regions. The non-linear impacts are the most significant in the agricultural freight generation sector
We examine a higher-order spatial autoregressive model with stochastic, but exogenous, spatial weigh...
In spatial econometrics the problem of stationarity has not received much attention. Typically, the ...
Abstract. Spatial regression models incorporating non-stationarity in the regression coefficients ar...
Covariance analysis is used to explore two questions encountered in modelling freight generation for...
In the context of modeling regional freight the four-stage model is a popular choice. The first stag...
We propose a semi-parametric spatial auto-covariance specification of the growth model to examine th...
This study is concerned with the development of models for total and commodity freight by road and b...
This paper investigates non-linearity in spatial processes models and allows for a gradual regime-sw...
Modeling regional economic dynamics requires the adoption of complex econometric tools, which allow...
In this paper, we propose a Bayesian estimation approach for a spatial autoregressive logit specific...
This paper introduces a new way of investigating linear and nonlinear Granger causality between expo...
Spatial modeling of economic phenomena requires the adoption of complex econometric tools, which all...
We exploit the information derived from geographical coordinates to endogenously identify spatial re...
Freight forecasting models are data intensive and require many explanatory variables to be accurate....
The stochastic frontier model with technical efficiency explained by exogenous variables is augmente...
We examine a higher-order spatial autoregressive model with stochastic, but exogenous, spatial weigh...
In spatial econometrics the problem of stationarity has not received much attention. Typically, the ...
Abstract. Spatial regression models incorporating non-stationarity in the regression coefficients ar...
Covariance analysis is used to explore two questions encountered in modelling freight generation for...
In the context of modeling regional freight the four-stage model is a popular choice. The first stag...
We propose a semi-parametric spatial auto-covariance specification of the growth model to examine th...
This study is concerned with the development of models for total and commodity freight by road and b...
This paper investigates non-linearity in spatial processes models and allows for a gradual regime-sw...
Modeling regional economic dynamics requires the adoption of complex econometric tools, which allow...
In this paper, we propose a Bayesian estimation approach for a spatial autoregressive logit specific...
This paper introduces a new way of investigating linear and nonlinear Granger causality between expo...
Spatial modeling of economic phenomena requires the adoption of complex econometric tools, which all...
We exploit the information derived from geographical coordinates to endogenously identify spatial re...
Freight forecasting models are data intensive and require many explanatory variables to be accurate....
The stochastic frontier model with technical efficiency explained by exogenous variables is augmente...
We examine a higher-order spatial autoregressive model with stochastic, but exogenous, spatial weigh...
In spatial econometrics the problem of stationarity has not received much attention. Typically, the ...
Abstract. Spatial regression models incorporating non-stationarity in the regression coefficients ar...