Spatial Econometric Models

LeSage, James P.; Pace, R. Kelley

doi:10.1007/978-3-642-03647-7_18

Cited by 227 publications

(146 citation statements)

References 26 publications

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“…1). It is known that models with spatially correlated residuals may result biased estimates of the parameters which may lead to erroneous interpretation and wrong conclusions (Anselin, 1988;LeSage, 1998). This could be one of the reasons why NB models showed the wrong sign in the coefficient of average speed.…”

Section: Non-spatial Modelsmentioning

confidence: 99%

“…According to LeSage (1998), two problems arise when sample data has a locational dimension: (1) spatial correlation exists between the observations, and (2) spatial heterogeneity occurs in the relationships that are modelled. Traditional econometrics (including NB models used in crash research) has largely ignored the issue of spatial correlation that violates the traditional Gauss-Markov assumptions used in regression modelling.…”

Section: Introductionmentioning

confidence: 99%

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Modelling area-wide count outcomes with spatial correlation and heterogeneity: An analysis of London crash data

Quddus

2008

Accident Analysis & Prevention

352

221

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Section: Non-spatial Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modelling area-wide count outcomes with spatial correlation and heterogeneity: An analysis of London crash data

Quddus

2008

Accident Analysis & Prevention

352

221

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“…One of the more basic problems is the existence of strong spatial relationships between observations. These relationships can violate the basic hypothesis of multiple linear regression model residual independence (LeSage and Pace, 2010). Anselin (1988) differentiates two basic types of spatial relationships.…”

Section: Introduction and Objectivesmentioning

confidence: 99%

Modelling transport and real-estate values interactions in urban systems

Ibeas

Cordera

dell’Olio

et al. 2012

Journal of Transport Geography

116

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a b s t r a c tThis article presents hedonic Multiple Linear Regression models (MLR), spatial autoregressive hedonic models (SAR), Spatial autoregressive hedonic in the Error term Models (SEMs) and spatial Durbin hedonic models (SDMs) to estimate house price variations in metropolitan areas as a result of changing environmental and accessibility conditions. The goodness of fit of the different models has been compared along with a series of hypotheses about the performance of the specifications considering spatial relationships between observations. The case study for such analysis was the urban area of Santander (Spain). The models which considered spatial dependence between observations offered a greater degree of fit in a scenario showing strong spatial correlation in MLR residuals. The SEM model combined with a QueenContiguity matrix provided a good fit to the data and at the same time presented significant parameters with theoretically coherent signs. This model estimated increases of 1.8% for each additional transit line present in the areas of housing, as well as a reduction of 1.1% in their prices for each additional minute in travelling time to the Central Business District. Closeness to the train stations, however, implied reductions in house prices.

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“…The initial estimated coefficients in the previous SDM (column 5, Table 4) did not represent marginal changes in the productivity growth as consequence of changes in the explicative variables, but these coefficients require a decomposition process of the total effect into direct and indirect effects (LeSage & Pace, 2010). The direct effect captures the effects of changes in the explicative variables in company i on the productivity growth of company i.…”

Section: Panel Estimation With Spatial Interaction Effects For Agri-fmentioning

confidence: 99%

The significance of the interconnection of second-level cooperatives and their peer-associated cooperatives for productivity growth

Martínez-Victoria

Maté‐Sánchez‐Val

Arcas‐Lario

2017

Span. j. agric. res.

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Cooperatives are especially important in current agri-food markets. These companies have responded to the current demand requirements with greater market orientation strategies to attract and satisfy customers. To do so, cooperatives have adopted different collaboration alternatives. In Spain, the most common alliance between cooperatives is materialised in second-level cooperatives, which are cooperatives integrated by at least two first-level cooperatives. The aim of this study was to analyse the interaction effects between first-and second level agri-food cooperatives on their productive growth and its components. To get this purpose, a CobbDouglas specification with spatial econometrics techniques was applied to evaluate this relationship. We included a spatial connectivity matrix to establish the interconnection among cooperatives of first-and second-level. Our results show a positive interaction effect highlighting the importance of these alliances on the productivity growth in the agri-food sector. The scarce amount of empirical papers explaining how second-level cooperatives influence the performance of first-level cooperatives shows the relevance of our study.Additional key words: Malmquist productivity index; spatial econometric modelling; spatial interaction effects.Correspondence should be addressed to María Martínez-Victoria: mdc.mvictoria@gmail.com

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Spatial Econometric Models

Cited by 227 publications

References 26 publications

Modelling area-wide count outcomes with spatial correlation and heterogeneity: An analysis of London crash data

Modelling area-wide count outcomes with spatial correlation and heterogeneity: An analysis of London crash data

Modelling transport and real-estate values interactions in urban systems

The significance of the interconnection of second-level cooperatives and their peer-associated cooperatives for productivity growth

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