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.

Regional electricity demand in Japan and spatial interaction among the regions using a Bayesian approach were examined. A spatial autoregressive (SAR) ARMA model was proposed to consider the features of electricity demand in Japan and a strategy of Markov chain Monte Carlo (MCMC) methods was...

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...

In this paper, we estimate the CO2 emission function based on the production function, taking into account the interregional spillover effect in Japan. Using the estimated result, we propose suitable means of simulation using spatial econometrics and simulate which prefecture can reduce the CO2...

We analyze the development of 18 types of criminal records in Japan for the period 1991–2001 across 47 prefectures with spatial lag and spatio-temporal heteroscedasticity. We explore the hypothesis that crime data are related to socio-economic variables in Japan. We extend the Bayesian...