Model Specification and Spatial Interdependence

Cook, Scott; Hays, Jude C.; Franzese, Robert J.

doi:10.4135/9781526486387.n42

Cited by 14 publications

(29 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In particular, we checked whether spatial autocorrelation in the outcome was caused by observable or unobservable effects. We found that the spatial-autoregressive lag model (which we did not apply) could be re-expressed as a higher-order variation of the SDEM (consistent with Franzese and Hays (2014) or Cook et al (2015)). Moreover, we calculated the means of the log-likelihood, the log-likelihood ratio, and the Moran's I of residuals, as recommended in LeSage (2014); Cook et al (2015); and Vega and Elhorst (2015).…”

Section: Discussionsupporting

confidence: 82%

“…We found that the spatial-autoregressive lag model (which we did not apply) could be re-expressed as a higher-order variation of the SDEM (consistent with Franzese and Hays (2014) or Cook et al (2015)). Moreover, we calculated the means of the log-likelihood, the log-likelihood ratio, and the Moran's I of residuals, as recommended in LeSage (2014); Cook et al (2015); and Vega and Elhorst (2015). All of the tests confirmed that the SDEM suits the context of regional matching functions better than the SLX, GNS, and SDM (see Table 5).…”

Section: Discussionsupporting

confidence: 82%

“…In our research, we employ a spatial Durbin panel fixed effects model with spatial error. According to the literature, this is the only model that produces unbiased estimates of parameters and correct standard errors (Cook, Hays, & Franzese, 2015;Elhorst, 2010;Franzese & Hays, 2014). This model allows for regional heterogeneity in the data; avoids bias in the non-spatial effects; and produces estimates of local spatial (spillover) effects and spatial effects in unobservables for particular matching functions.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Unemployment and vacancy flows in spatial labour market matching at the regional level. The case of a transition country

Antczak

Gałecka‐Burdziak

Pater

2018

Journal of Applied Economics

View full text Add to dashboard Cite

We examine the extent to which spatial interactions affect the labour market matching process at the regional level using three frameworks: random, stock-flow, and job-queuing. We study the underexplored, regionally diversified former transition country of Poland at the LAU-1 level over the 2003-2014 time period using registered monthly data. We employ a Durbin panel model with spatial error. Results prove that spatial interactions affect matching and that the stock-flow model is in some respects superior to others in explaining the job creation process. Unemployment affects matching more than vacancies. The newly unemployed may seek jobs in adjacent regions and can cause congestion there, but vacancies tend to attract workers from adjacent markets and exert positive externalities. Policy actions should be aimed at exploiting spatial interdependencies to improve matching at the regional level by increasing the number of available job offers, improving information, and increasing labour force mobility.

show abstract

Section: Discussionsupporting

confidence: 82%

Section: Discussionsupporting

confidence: 82%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Unemployment and vacancy flows in spatial labour market matching at the regional level. The case of a transition country

Antczak

Gałecka‐Burdziak

Pater

2018

Journal of Applied Economics

View full text Add to dashboard Cite

show abstract

“…SDEM is a combination of the spatially lagged-X (SLX) model that assumes the effect of the explanatory variables of the neighboring areas on the outcome variable of the area (spillover effect), and the spatial error model (SEM), which assumes the interaction between the unobserved variables in neighboring areas and the area (46)(47)(48). We believe that the local spillover effect was more appropriate as the outcome variable of the current study was a social aggregate (49,50). We constructed the row-standardized queen contiguity spatial weights of This study used the "splm" package in R 3.6.3 (https://www.r-project.org), and QGIS 3.12.1…”

Section: Discussionmentioning

confidence: 99%

Spatial Distribution of Neighborhood-level Housing Prices and Its Association With All-cause Mortality in Seoul, Korea (2013–2018): a Spatial Panel Data Analysis

Kim

2020

Preprint

View full text Add to dashboard Cite

Background: Housing prices are known to be a relevant indicator of the socioeconomic position of the neighborhood. In a society where the market system mainly drives housing prices, residents' spatial patterning is formulated according to their socioeconomic position. Methods: Dividing the 2013–2018 entire study period into three periods, we explored the geographical distribution of housing prices and all-cause mortality and their association in Seoul, where the country's capital city. The government authorities' data and 2015 census data were used for the study. We mapped the geographical distribution of housing prices and all-cause mortality and investigated the changes in distribution. We conducted a pooled OLS and spatial panel regression analysis to estimate housing prices elasticity of all-cause mortality. We also explored the possible mediating role of housing prices on the educational composition's effect on all-cause mortality. Results: We found the common trends of increasing spatial patterning of housing prices and all-cause mortality. The magnitude of spatial patterning was far greater in housing prices than all-cause mortality. In a pooled OLS regression analysis, we found that a 1% increase in housing price was associated with a 0.11% reduction in all-cause mortality after controlling the explanatory variables. Attenuation in the regression coefficient's magnitude was found after adding the neighborhood's educational composition in the model. As a result of spatial panel analysis, we found a direction and scale similar to the housing price elasticity of all-cause mortality in the final pooled OLS model. Conclusions: The results suggested that geographical health inequality in Korea's urban space mainly stemmed from the affinity of high socioeconomic position and the more developed spaces.

show abstract

“…The growth in the use of spatial econometric models in political science is partly a consequence of improvements in the techniques used to estimate and interpret increasingly complex specifications (e.g., Beck et al, 2006;Franzese and Hays, 2006;Ward and Gleditsch, 2008;Darmofal, 2015). Though the theories used to justify spatial models are varied (see e.g., Shipan and Volden, 2008, for policy diffusion), these models typically focus on three types of spatial processes (Cook et al, 2015): clustering in the outcomes (i.e., when y i influences y j and vice versa), clustering in the unobservables (i.e., when ϵ i is correlated with ϵ j ), and clustering in the observables (i.e., when x i influences y j ). Most political science research has focused on two particular models, the SAR and the spatial-X (SLX) models.…”

Section: Spatial Econometric Modelsmentioning

confidence: 99%

Interpretation: the final spatial frontier

Whitten

Williams

Wimpy

2019

PSRM

View full text Add to dashboard Cite

The use of spatial econometric models in political science has steadily risen in recent years. However, the interpretation of these models has generally ignored the important substantive, and even spatial, nature of the estimated effects. This leaves many papers with a (non-spatial) interpretation of coefficients on the covariates and a brief discussion of the sign and strength of the spatial parameter. We introduce a general approach to interpreting spatial models and provide several avenues for an exposition of substantive spatial effects. Our approach can be generalized to most models in the spatial econometric taxonomy. Building on the example of the diffusion of democracy, we elucidate how our approach can be applied to modern political science problems.

show abstract

Model Specification and Spatial Interdependence

Cited by 14 publications

References 0 publications

Unemployment and vacancy flows in spatial labour market matching at the regional level. The case of a transition country

Unemployment and vacancy flows in spatial labour market matching at the regional level. The case of a transition country

Spatial Distribution of Neighborhood-level Housing Prices and Its Association With All-cause Mortality in Seoul, Korea (2013–2018): a Spatial Panel Data Analysis

Interpretation: the final spatial frontier

Contact Info

Product

Resources

About