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Modeling Non-Linear Spatial Dynamics: A Family of Spatial STAR Models and an Application to U.S. Economic Growth

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Author Info
Pede, Valerien O.
Florax, Raymond J.G.M.
Holt, Matthew T.

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Abstract

This paper investigates non-linearity in spatial processes models and allows for a gradual regime-switching structure in the form of a smooth transition autoregressive process. Until now, applications of the smooth transition autoregressive (STAR) model have been largely confined to the time series context. The paper focuses on extending the non-linear smooth transition perspective to spatial processes models, in which spatial correlation is taken into account through the use of a so-called weights matrix identifying the topology of the spatial system. We start by deriving a non-linearity test for a simple spatial model, in which spatial correlation is only included in the transition function. Next, we propose a non-linearity test for a model that includes a spatially lagged dependent variable or spatially autocorrelated innovations as well. Monte Carlo simulations of the various test statistics are performed to examine their power and size. The proposed modeling framework is then used to identify convergence clubs in the context of U.S. county-level economic growth over the period 1963–2003.

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Publisher Info
Paper provided by American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association) in its series 2008 Annual Meeting, July 27-29, 2008, Orlando, Florida with number 6518.

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Date of creation: 2008
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Handle: RePEc:ags:aaea08:6518

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Related research
Keywords: spatial econometrics; non-linearity; utoregressive smooth transition; Research Methods/ Statistical Methods; C12; C21; C51; O18; R11;

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  1. Terasvirta, T & Anderson, H M, 1992. "Characterizing Nonlinearities in Business Cycles Using Smooth Transition Autoregressive Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(S), pages S119-36, Suppl. De. [Downloadable!] (restricted)
  2. Fabio Canova, 2004. "Testing for Convergence Clubs in Income Per Capita: A Predictive Density Approach," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 45(1), pages 49-77, 02. [Downloadable!] (restricted)
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  3. Conley, Timothy G. & Molinari, Francesca, 2005. "Spatial Correlation Robust Inference with Errors in Location or Distance," Working Papers 05-12, Cornell University, Center for Analytic Economics. [Downloadable!]
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  4. Sergio J. Rey, Brett D. Montouri, 1999. "US Regional Income Convergence: A Spatial Econometric Perspective," Regional Studies, Taylor and Francis Journals, vol. 33(2), pages 143-156, April. [Downloadable!] (restricted)
  5. Ahmad Baharumshah & Venus Liew, 2006. "Forecasting Performance of Exponential Smooth Transition Autoregressive Exchange Rate Models," Open Economies Review, Springer, vol. 17(2), pages 235-251, April. [Downloadable!] (restricted)
  6. Julie Le Gallo & Sandy Dall'erba, 2006. "Evaluating the Temporal and Spatial Heterogeneity of the European Convergence Process, 1980-1999," Journal of Regional Science, Blackwell Publishing, vol. 46(2), pages 269-288. [Downloadable!] (restricted)
  7. Matthew T. Holt & Lee A. Craig, 2006. "Nonlinear Dynamics and Structural Change in the U.S. Hog-Corn Cycle: A Time-Varying STAR Approach," American Journal of Agricultural Economics, American Agricultural Economics Association, vol. 88(1), pages 215-233, 02. [Downloadable!] (restricted)
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