Testing Impact Measures in Spatial Autoregressive Models


Creative Commons License

Arbia G., Bera A. K., Doğan O., Taşpınar S.

International Regional Science Review, vol.43, no.1-2, pp.40-75, 2020 (SSCI) identifier

  • Publication Type: Article / Article
  • Volume: 43 Issue: 1-2
  • Publication Date: 2020
  • Doi Number: 10.1177/0160017619826264
  • Journal Name: International Regional Science Review
  • Journal Indexes: Social Sciences Citation Index (SSCI), Scopus, Academic Search Premier, IBZ Online, International Bibliography of Social Sciences, ABI/INFORM, Business Source Elite, Business Source Premier, CAB Abstracts, EconLit, Geobase, PAIS International, Sociological abstracts
  • Page Numbers: pp.40-75
  • Keywords: asymptotic approximation, direct effects, impact measures, indirect effects, inference, MLE, spatial autoregressive models, spatial econometric models, standard errors, total effects
  • Istanbul Technical University Affiliated: Yes

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.