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...

We show that the ordinary least-squares method used by Pirinsky and Wang (2006) suffers from an endogeneity problem leading to biased and inconsistent estimates. A maximum likelihood estimation procedure that will produce consistent estimates for their model is set forth. This is done using an...

We explore origin–destination forecasting of commodity flows between 15 Spanish regions, using data covering the period from 1995 to 2004. The 1-year-ahead forecasts are based on a recently introduced spatial autoregressive variant of the traditional gravity model. Gravity (or spatial...

We introduce a Bayesian hierarchical regression model that extends the traditional least-squares regression model used to estimate gravity or spatial interaction relations involving origin-destination flows. Spatial interaction models attempt to explain variation in flows from n origin regions...

This study investigates the pattern of knowledge spillovers arising from patent activity between European regions. A Bayesian hierarchical model is developed that specifies region-specific latent effects parameters modeled using a connectivity structure between regions that can reflect...

We extend the literature on Bayesian model comparison for ordinary least-squares regression models to include spatial autoregressive and spatial error models. Our focus is on comparing models that consist of different matrices of explanatory variables. A Markov Chain Monte Carlo model...