Likelihood functions of spatial autoregressive models with normal but heteroskedastic disturbances have been already derived [Anselin (1988, ch.6)]. But there is no implementation for maximum likelihood estimation of these likelihood functions in general (heteroskedastic disturbances) cases....

Likelihood functions of spatial autoregressive models with normal but heteroskedastic disturbances have been already derived [Anselin (1988, ch.6)]. But there is no implementation for maximum likelihood estimation of these likelihood functions in general (heteroskedastic disturbances) cases....

In this research, the omitted variable problem in a spatial autoregressive model is analyzed by simulation. We examine the performances of estimators when an omitted variable is correlated with explanatory variables. In the literature, theoretical aspects of estimating spatial autoregressive...

Empirical analyses on urban/regional amenity began in the 1980s. These economic researches measured the “quality of life index” (QOLI) of cities and regions. The endogenous variables, rent, wage and population, were explained by social and economical attributes as well as some amenity...

In spatial autoregressive models, spatial autocorrelations in the dependent (or omitted) variable are modeled. Dependency is measured under known spatial structures, typically represented as a spatial weight matrix (W). For ordinal spatial autoregressive models, a unique W exists, and the...

The likelihood functions for spatial autoregressive models with normal but heteroskedastic disturbances have been derived [Anselin (1988, ch.6)], but there is no implementation of maximum likelihood estimation for these likelihood functions in general cases with heteroskedastic disturbances....