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Local and global spatial effects in hierarchical models

Author

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  • Donald J. Lacombe
  • Stuart G. McIntyre

Abstract

Hierarchical models have a long history in empirical applications; recognition of the fact that many datasets of interest to applied econometricians are nested; counties within states, pupils within school, regions within countries, etc. Just as many datasets are characterized by nesting, many are also characterized by the presence of spatial dependence or spatial heterogeneity. Significant advances have been made in developing econometric techniques and models to allow applied econometricians to address this spatial dimension to their data. This article fuses these two literatures together and combines a hierarchical model with the two general spatial econometric models.

Suggested Citation

  • Donald J. Lacombe & Stuart G. McIntyre, 2016. "Local and global spatial effects in hierarchical models," Applied Economics Letters, Taylor & Francis Journals, vol. 23(16), pages 1168-1172, November.
    • Handle: RePEc:taf:apeclt:v:23:y:2016:i:16:p:1168-1172
    DOI: 10.1080/13504851.2016.1142645
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    References listed on IDEAS

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    1. Franzese, Robert J., 2005. "Empirical Strategies for Various Manifestations of Multilevel Data," Political Analysis, Cambridge University Press, vol. 13(4), pages 430-446.
    2. James P. LeSage, 2014. "What Regional Scientists Need to Know about Spatial Econometrics," The Review of Regional Studies, Southern Regional Science Association, vol. 44(1), pages 13-32, Spring.
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    Cited by:

    1. Christian F. Hirche & Tammo H. A. Bijmolt & Maarten J. Gijsenberg, 2022. "When Offline Stores Reduce Online Returns," Sustainability, MDPI, vol. 14(13), pages 1-26, June.
    2. Rubén Ferrer Velasco & Margret Köthke & Melvin Lippe & Sven Günter, 2020. "Scale and context dependency of deforestation drivers: Insights from spatial econometrics in the tropics," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-32, January.
    3. Nikolas Kuschnig, 2022. "Bayesian spatial econometrics: a software architecture," Journal of Spatial Econometrics, Springer, vol. 3(1), pages 1-25, December.
    4. Joshua C. Hall & Donald J. Lacombe & Amir Neto & James Young, 2022. "Bayesian Estimation of the Hierarchical SLX Model with an Application to Housing Markets," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 46(2), pages 360-373, April.

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