Abstract
ABSTRACT This study examines the problem of parameter estimation in spatial econometric/social interaction models with non-random missing outcome data. First, we construct a sample selection model considering spatial lag (autoregressive) dependence. Then, we suggest a parameter estimation method for this model by slightly modifying the Bayesian Markov chain Monte Carlo algorithm proposed in an existing study. A simple illustration indicates that the proposed parameter estimation method performs well overall if the spatial autocorrelation is moderate (spatial parameter equals 0.5 or less), even under a relatively high missing data ratio (around 40%).
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