A Spatial Econometric Star Model With An Application To U.S. County Economic Growth, 1969-2003
Spatial regression models incorporating non-stationarity in the regression coefficients are popular. We propose a spatial variant of the Smooth Transition AutoRegressive (STAR) model that is more parsimonious than commonly used approaches and endogenously determines the extent of spatial parameter variation. Uncomplicated estimation and inference procedures are demonstrated using a neoclassical convergence model for United States counties.
|Date of creation:||2009|
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- Anselin, Luc & Bera, Anil K. & Florax, Raymond & Yoon, Mann J., 1996. "Simple diagnostic tests for spatial dependence," Regional Science and Urban Economics, Elsevier, vol. 26(1), pages 77-104, February.
- Roberto BASILE & Bernard GRESS, 2005. "Semi-Parametric Spatial Auto-Covariance Models Of Regional Growth In Europe," Region et Developpement, Region et Developpement, LEAD, Universite du Sud - Toulon Var, vol. 21, pages 93-118.
- Roberto Basile, 2008. "Regional economic growth in Europe: A semiparametric spatial dependence approach," Papers in Regional Science, Wiley Blackwell, vol. 87(4), pages 527-544, November.
- Seong-Hoon Cho & Dayton Lambert & Zhuo Chen, 2010. "Geographically weighted regression bandwidth selection and spatial autocorrelation: an empirical example using Chinese agriculture data," Applied Economics Letters, Taylor & Francis Journals, vol. 17(8), pages 767-772.
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