A number of authors have suggested that omitted variables affect spatial regression methods less than ordinary least-squares (OLS). To explore these conjectures, we derive an expression for OLS omitted variable bias in a univariate model with spatial dependence and show that positive dependence...

While salary benchmarking is widely used to help set compensation, there has been little econometric modeling of peer-group identification and the impacts of benchmarking on the compensation decision. We adapt some empirical tools from spatial econometrics to analyze compensation decisions...

We show how to quickly estimate spatial probit models for large data sets using maximum likelihood. Like Beron and Vijverberg (2004), we use the GHK (Geweke-Hajivassiliou-Keane) algorithm to perform maximum simulated likelihood estimation. However, using the GHK for large sample sizes has been...

Often, authors report materially different OLS and spatial error model estimates. However, under the null of correct specification, these estimates should be similar. We propose a spatial Hausman test and conduct a Monte Carlo experiment to examine its performance.