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...

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...

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...

There is near universal agreement that estimates and inferences from spatial regression models are sensitive to particular specifications used for the spatial weight structure in these models. We find little theoretical basis for this commonly held belief, if estimates and inferences are based...

<title>Abstract</title> Spatial filtering in various forms has become a popular way to address spatial dependence in statistical models (Griffith, 2003; Tiefelsdorf & Griffith, 2007). However, spatial filtering faces computational challenges for large <italic>n</italic> as the current method requires order of n-super-3 operations....

Standard spatial autoregressive models rely on spatial weight structures constructed to model dependence among "n" regions. Ways of parsimoniously modeling the connectivity among the sample of <formula format="inline"><simplemath>"N"&equals;"n"-super-2</simplemath></formula> origin-destination (OD) pairs that arise in a closed system of interregional flows has...