 Testing Impact Measures in Spatial Autoregressive ModelsGiuseppe Arbia (), Anil K. Bera, Osman Doğan and Süleyman Taşpınar International Regional Science Review, 2020, vol. 43, issue 1-2, 40-75 Abstract: Researchers often make use of linear regression models in order to assess the impact of policies on target outcomes. In a correctly specified linear regression model, the marginal impact is simply measured by the linear regression coefficient. However, when dealing with both synchronic and diachronic spatial data, the interpretation of the parameters is more complex because the effects of policies extend to the neighboring locations. Summary measures have been suggested in the literature for the cross-sectional spatial linear regression models and spatial panel data models. In this article, we compare three procedures for testing the significance of impact measures in the spatial linear regression models. These procedures include (i) the estimating equation approach, (ii) the classical delta method, and (iii) the simulation method. In a Monte Carlo study, we compare the finite sample properties of these procedures. Keywords: spatial econometric models; spatial autoregressive models; impact measures; asymptotic approximation; standard errors; inference; MLE; direct effects; indirect effects; total effects (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:sae:inrsre:v:43:y:2020:i:1-2:p:40-75 Access Statistics for this article More articles in International Regional Science Review |
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