  A partially linear probit model for spatially dependent data is considered. A
triangular array setting is used to cover various patterns of spatial data.
Conditional spatial heteroscedasticity and non-identically distributed
observations and a linear process for disturbances are assumed, allowing
various spatial dependencies. The estimation procedure is a combination of a
weighted likelihood and a generalized method of moments. The procedure first
fixes the parametric components of the model and then estimates the
non-parametric part using weighted likelihood; the obtained estimate is then
used to construct a GMM parametric component estimate. The consistency and
asymptotic distribution of the estimators are established under sufficient
conditions. Some simulation experiments are provided to investigate the finite
sample performance of the estimators.
