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A General Misspecification Test for Spatial Regression Models: Dependence, Heterogeneity, and Nonlinearity

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Author Info

  • Thomas de Graaff
  • Raymond J.C.M. Florax
  • Peter Nijkamp
  • Aura Reggiani

Abstract

There is an increasing awareness of the potentials of nonlinear modeling in regional science. This can be explained partly by the recognition of the limitations of conventional equilibrium models in complex situations, and also by the easy availability and accessibility of sophisticated computational techniques. Among the class of nonlinear models, dynamic variants based on, for example, chaos theory stand out as an interesting approach. However, the operational significance of such approaches is still rather limited and a rigorous statistical-econometric treatment of nonlinear dynamic modeling experiments is lacking. Against this background this paper is concerned with a methodological and empirical analysis of a general misspecification test for spatial regression models that is expected to have power against nonlinearity, spatial dependence, and heteroskedasticity. The paper seeks to break new research ground by linking the classical diagnostic tools developed in spatial econometrics to a misspecification test derived directly from chaos theory-the BDS test, developed by Brock, Dechert, and Scheinkman (1987). A spatial variant of the BDS test is introduced and applied in the context of two examples of spatial process models, one of which is concerned with the spatial distribution of regional investments in The Netherlands, the other with spatial crime patterns in Columbus, Ohio. Copyright 2001 BlackwellPublishers

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Bibliographic Info

Article provided by Wiley Blackwell in its journal Journal of Regional Science.

Volume (Year): 41 (2001)
Issue (Month): 2 ()
Pages: 255-276

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Handle: RePEc:bla:jregsc:v:41:y:2001:i:2:p:255-276

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Cited by:
  1. Jeremy Porter, 2012. "A Simplified Indicator of Social Well-Being in the United States: Examining the Ecological Impact of Family Formation within a County Level Framework," Social Indicators Research, Springer, vol. 108(3), pages 421-440, September.
  2. Raymond J.G.M. Florax & Hendrik Folmer & Sergio J. Rey, 2002. "Specification Searches in Spatial Econometrics: The Relevance of Hendry's Methodology," Urban/Regional 0202001, EconWPA.
  3. Brown, Jason P. & Lambert, Dayton M., 2009. "Short-run Birth and Death of U.S. Manufacturing Firms: 2000 - 2005," 2009 Annual Meeting, January 31-February 3, 2009, Atlanta, Georgia 46739, Southern Agricultural Economics Association.
  4. Brown, Jason P. & Lambert, Dayton M. & Florax, Raymond J.G.M., 2010. "Manufacturing Transition in Local Economies: A Regional Adjustment Model," 2010 Annual Meeting, July 25-27, 2010, Denver, Colorado 61130, Agricultural and Applied Economics Association.
  5. Guangqing Chi & Jun Zhu, 2008. "Spatial Regression Models for Demographic Analysis," Population Research and Policy Review, Springer, vol. 27(1), pages 17-42, February.
  6. repec:dgr:uvatin:2007065 is not listed on IDEAS
  7. Johan Lundberg, 2006. "Spatial interaction model of spillovers from locally provided public services," Regional Studies, Taylor & Francis Journals, vol. 40(6), pages 631-644.
  8. Marko Kryvobokov, 2010. "Is it worth identifying service employment (sub)centres when modelling apartment prices?," Post-Print halshs-00577899, HAL.
  9. Porter, Jeremy R. & Purser, Christopher W., 2010. "Social disorganization, marriage, and reported crime: A spatial econometrics examination of family formation and criminal offending," Journal of Criminal Justice, Elsevier, vol. 38(5), pages 942-950, September.

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