This paper investigates the adequacy of the matrix exponential spatial specifications (MESS) as an alternative to the widely used spatial autoregressive models (SAR). To provide as complete a picture as possible, we extend the analysis to all the main spatial models governed by matrix exponentials comparing them with their spatial autoregressive counterparts. We propose a new implementation of Bayesian parameter estimation for the MESS model with vague prior distributions, which is shown to be precise and computationally efficient. Our implementations also account for spatially lagged regressors. We further allow for location-specific heterogeneity, which we model by including spatial splines. We conclude by comparing the perform...
This thesis addresses issues in the econometric analysis of data observed over regular or irregular ...
The aim of this paper is to define and to analyse classes of models satisfactory for the considerati...
This paper considers the most important aspects of model uncertainty for spatial regression models,...
This paper investigates the adequacy of the matrix exponential spatial specifications (MESS) as an ...
The matrix exponential spatial specification (MESS) is an alternative to the spatial autoregressive-...
International audienceThis paper studies large sample properties of the matrix exponential spatial s...
In this paper we analyze the partial and marginal covariance structures of the spatial model with th...
We examine a higher-order spatial autoregressive model with stochastic, but exogenous, spatial weigh...
We examine a higher-order spatial autoregressive model with stochastic, but exogenous, spatial weigh...
This paper studies the correlation structure of spatial autoregressions defined over arbitrary confi...
This paper develops consistency and asymptotic normality of parameter estimates for a higher-order s...
This article deals with symmetrical data that can be modelled based on Gaussian distribution. We con...
We use moments from the covariance matrix for spatial panel data to estimate the param-eters of the ...
Spatial lag dependence in a regression model is similar to the inclusion of a serially autoregressiv...
This paper develops consistency and asymptotic normality of parameter estimates for a higher-order s...
This thesis addresses issues in the econometric analysis of data observed over regular or irregular ...
The aim of this paper is to define and to analyse classes of models satisfactory for the considerati...
This paper considers the most important aspects of model uncertainty for spatial regression models,...
This paper investigates the adequacy of the matrix exponential spatial specifications (MESS) as an ...
The matrix exponential spatial specification (MESS) is an alternative to the spatial autoregressive-...
International audienceThis paper studies large sample properties of the matrix exponential spatial s...
In this paper we analyze the partial and marginal covariance structures of the spatial model with th...
We examine a higher-order spatial autoregressive model with stochastic, but exogenous, spatial weigh...
We examine a higher-order spatial autoregressive model with stochastic, but exogenous, spatial weigh...
This paper studies the correlation structure of spatial autoregressions defined over arbitrary confi...
This paper develops consistency and asymptotic normality of parameter estimates for a higher-order s...
This article deals with symmetrical data that can be modelled based on Gaussian distribution. We con...
We use moments from the covariance matrix for spatial panel data to estimate the param-eters of the ...
Spatial lag dependence in a regression model is similar to the inclusion of a serially autoregressiv...
This paper develops consistency and asymptotic normality of parameter estimates for a higher-order s...
This thesis addresses issues in the econometric analysis of data observed over regular or irregular ...
The aim of this paper is to define and to analyse classes of models satisfactory for the considerati...
This paper considers the most important aspects of model uncertainty for spatial regression models,...