Abstract This article considers autoregressive (SAR) models. We method to estimate the parameters of likelihood (ML) method. Our Bayesian by the Monte Carlo studies. We found the efficient as the ML estimators.

We study how the urban context of a disaster affects protection decisions against catastrophes using the 1995 Kobe earthquake, which is highly relevant to urban life but difficult to link with rural life. We use a Bayesian approach to identify regions with major cities where the sensitivities of...

This paper considers the distance functional weight matrix in spatial autoregressive and spatial error models from a Bayesian point of view. We considered the Markov chain Monte Carlo methods to estimate the parameters of the models. Our approach is illustrated with simulated data set.

We suggest a new class of cross-sectional space-time models based on local AR models and nearest neighbors using distances between observations. For the estimation we use a tightness prior for prediction of regional GDP forecasts. We extend the model to the model with exogenous variable model...