Using the well-known Harrison and Rubinfeld (1978) hedonicpricing data, this manuscript demonstrates the substantialbenefits obtained by modeling the spatial dependence of theerrors. Specifically, the estimated errors on the spatialautogregression fell by 44% relative to OLS. The...

This paper provides various paradigms for the grid estimator; the most useful being a representation of the grid estimator as a combination of the nonparametric nearest neighbor estimator and a parametric estimator. Hence, the grid estimator falls into the class of semiparametric estimators. The...

Most hedonic pricing studies using transaction data employ only sold properties. Since the properties sold during any year or even decade represent only a fraction of all properties, this approach ignores the potentially valuable information content of unsold properties which have known...

Parametric estimators, such as OLS, attain high efficiency for well-specified models. Nonparametric estimators greatly reduce specification error but at the cost of efficiency. Semiparametric estimators compromise between these dual goals of efficiency and specification error. Semiparametric...

Using 70,822 observations on housing prices during 1969-91 from Fairfax County Virginia, this manuscript demonstrates the substantial benefits obtained by modeling the spatial as well as the temporal dependence of the data. Specifically, the spatio temporal autoregression with 12 variables...

Given local spatial error dependence, one can construct sparse spatial weight matrices. As an illustration of the power of such sparse structures, we computed a simultaneous autoregression using 20 640 observations in under 19 min despite needing to compute a 20 640 by 20 640 determinant 10 times.

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.