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....

Currently, there are a number of environmental issues that are of global concern, such as global warming, acid rain and ozone layer depletion. The resulting damages are not necessarily confined to only the country where the pollutants are emitted. Hence, multiregional and multilateral...

In the context of spatial econometrics, we discuss the specification of one-directional effects, not mutual dependencies. Using an empirical study (a spatial autoregressive model of land price data in Fukui Prefecture, Japan) and Monte Carlo simulation results (contiguity matrices built based on...

This paper numerically evaluates the efficiency of regulations on building size and city size in a congested closed city by comparing welfare gain with that achieved under a first-best toll regime. Results show that whereas the urban growth boundary (UGB) is a poor substitute for the toll regime...

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....