Raising the bar (1)

Elhorst, J. Paul; Abreu, Maria; Amaral, Pedro; Bhattacharjee, Arnab; Corrado, Luisa; Fingleton, Bernard; Fuerst, Franz; Garretsen, J.H.; Igliori, D.; Gallo, Julie Le; McCann, Philip; Monastiriotis, Vassilis; Pryce, Gwilym; Yu, Jihai

doi:10.1080/17421772.2015.1126966

Cited by 6 publications

(2 citation statements)

References 28 publications

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“…Given the lack of prior theoretical knowledge about the correct model and spatial weights matrix combination to choose, this paper follows LeSage's (2014LeSage's ( , 2015 method for computing Bayesian posterior model probabilities to compare three types of pooled models (containing observations for all five regions)the SLX, SDM and SDEMand a range of spatial weights matrices, from three to 20 nearest-neighbours 7 for each dependent variable of interest. While alternative methods for solving the model comparison problem have been proposed (Elhorst et al, 2016), including a non-Bayesian method from Gerkman and Ahlgren (2014), LeSage's approach is (to this point) the most theoretically Notes: a Based on a comparison with non-spatial Poisson regression run in R using all observations and the corresponding background population measure (i.e., acres, existing businesses, population, new businesses) as the offset variable. Variables chosen for regression are based on correlation analysis using the full data set.…”

Section: Spatial Regression Specificationmentioning

confidence: 99%

Transitive properties: a spatial econometric analysis of new business creation around transit

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2018

Spatial Economic Analysis

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This paper evaluates the relationship between transit station proximity and new business creation in five US regions with varying levels of maturity in rail transit development and/or entrepreneurial ecosystems: Boston, San Jose, Austin, Cleveland and Philadelphia. It tests a variety of spatial econometric models to find the best specification and compares the results with the kinds of non-spatial models currently used in the literature. This provides a better understanding of the role of various forms of spatial dependence in the transitnew business creation relationship and shows that existing models may overstate the impact of transit on new business creation. In addition, the paper teases out differences between regions, rail modes and business types that can be usefully applied to a variety of urban contexts. KEYWORDS spatial models (C21), economic development (F63), urban, rural, regional, and transportation analysis (O18), planning policy (O21) HISTORY

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Section: Spatial Regression Specificationmentioning

confidence: 99%

Transitive properties: a spatial econometric analysis of new business creation around transit

Credit

2018

Spatial Economic Analysis

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“…Only recently have attempts been made in different directions in order to abandon the exogeneity assumption. This topic was recently discussed and illustrated with examples in the editorial to issue 11(1) of this journal (Elhorst et al, 2016).…”

Section: Raising the Bar (3)mentioning

confidence: 99%