Smoothed Spatial Maximum Score Estimation of Spatial Autoregressive Binary Choice Panel Models

Abstract: This paper considers spatial autoregressive (SAR) binary choice models in the context of panel data with fixed effects, where the latent dependent variables are spatially correlated. Without imposing any parametric structure of the error terms, this paper proposes a smoothed spatial maximum score (SSMS) estimator which consistently estimates the model parameters up to scale. The identification of parameters is obtained, when the disturbances are time-stationary and the explanatory variables vary enough over time along with an exogenous and time-invariant spatial weight matrix. Consistency and asymptotic distribution of the proposed estimator are also derived in the paper. Finally, a Monte Carlo study indicates that the SSMS estimator performs quite well in finite samples.