A Spatial Econometric Star Model With An Application To U.S. County Economic Growth, 1969-2003
AbstractSpatial 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.
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Bibliographic InfoPaper provided by Purdue University, College of Agriculture, Department of Agricultural Economics in its series Working Papers with number 09-03.
Length: 23 pages
Date of creation: 2009
Date of revision:
spatial autoregression; smooth transition; spatial econometrics; STAR; GWR;
Find related papers by JEL classification:
- C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
- R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes
- R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)
This paper has been announced in the following NEP Reports:
- NEP-ALL-2009-04-13 (All new papers)
- NEP-ECM-2009-04-13 (Econometrics)
- NEP-MKT-2009-04-13 (Marketing)
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- 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.
- Maria Abreu & Henri L.F. de Groot & Raymond J.G.M. Florax, 2004. "Space and Growth," Tinbergen Institute Discussion Papers 04-129/3, Tinbergen Institute.
- 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.
- Brown, Jason & Lambert, Dayton, 2014. "Location decisions of natural gas extraction establishments: a smooth transition count model approach," Research Working Paper RWP 14-5, Federal Reserve Bank of Kansas City.
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