Search

Ordinary least squares econometric approaches to estimating election vote outcomes potentially ignore spatial dependence (or autocorrelation) in the data that may affect estimates of voting behavior. The presence of spatial autocorrelation in the data can yield biased or inconsistent point estimates when ordinary least squares is used inappropriately. Therefore, this paper puts forward a spatial econometric model to estimate the vote outcomes in the 2004 presidential election. We contribute to the literature in two ways. One, we extend the voting behavior literature by considering newly developed spatial specification tests to determine the proper econometric model. The results of two different spatial specification tests suggest that a spatial Durbin model provides a better fit to the data. Two, we offer a richer interpretation of the spatial effects, which differ from standard ordinary least squares estimates, of the county-level vote outcome for the 2004 presidential election.

This study offers a unique contribution to the literature by investigating the convergence of province-level carbon dioxide emission intensity among a panel of 30 provinces in China over the period 1990-2010. We use a novel, spatial dynamic panel data model to evaluate an empirically testable hypothesis of convergence among provinces. Our results suggest that: (1) CO2 emission intensities are converging across provinces in China; (2) the rate of convergence is higher with the dynamic panel data model than the cross-sectional regression models; and, (3) province-level CO2 emission intensities are spatially correlated and the rate of convergence, when controlling for spatial autocorrelation, is higher than with the non-spatial models.

Spatial dependence is an important factor in regional economic growth analysis, especially in terms of population, employment, and per capita income. This paper employs spatial econometric techniques and U.S. Census Bureau county-level data for the period of 1980-2010 to identify and estimate the impacts of residential real estate investment on the economic development of the Northeast region. A spatial panel method is used to analyze the spillover effect of county level economic development on neighboring counties

The Supplemental Nutrition Assistance Program (SNAP) is a federal program that provides assistance to low- and no-income people in the United States. Its aim is to increase individuals’ food-purchasing power and improve the nutritional content of their diet. We employed recent advances in Bayesian spatial econometric modeling to determine the appropriate model for drawing inferences about the percentage of SNAP recipients in Appalachia. We found that there is significant spatial dependence justifying the use of spatial econometric methods. We also examined how changes in an independent variable affect the dependent variable for orders of neighbors over space.

We survey the literature on spatial bio-economic and land-use modelling and review thematic developments. Unobserved site-specific heterogeneity is common in almost all of the surveyed works. Heterogeneity appears also to be a significant catalyst engendering significant methodological innovation. To better equip prototypes to adequately incorporate heterogeneity, we consider a smorgasbord of extensions. We highlight some problems arising with their application; provide Bayesian solutions to some; and conjecture solutions for others.