Estimating regression coefficients by W-based and latent variables spatial autoregressive models in the presence of spillovers from hotspots: Evidence from Monte Carlo simulations

The paper evaluates by means of Monte Carlo simulations the estimators of regression coefficients in the presence of spillover effects from one or more hotspots by the classical W-based spatial autoregressive model and the structural equation model with latent variables (SEM). The estimators are evaluated in terms of bias and root mean squared error (RMSE) for different values of the spatial autoregressive coefficient, different sample sizes and different specifications of weight matrices. The simulation results show that both approaches perform better for smaller values of the spatial autoregressive coefficient and larger sample sizes. SEM tends to outperform the classical approach in term of bias but the classical model based on first-order contiguity matrix has lowest RMSE in most cases. Furthermore, SEM provides a more stable performance in terms of variations of bias and RMSE with respect to changes in the value of autoregressive coefficient, sample size and number of hotspots. It follows that compared to the classical approach, SEM does not only have favorable behavioral properties in that it straightforwardly allows inclusion of different types of spatial dependence in one model framework and of testing distance decay, but also favorable econometric properties.