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A General Misspecification Test for Spatial Regression Models: Dependence, Heterogeneity, and Nonlinearity
Author(s) -
De Graaff Thomas,
Florax Raymond J.C.M.,
Nijkamp Peter,
Reggiani Aura
Publication year - 2001
Publication title -
journal of regional science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.171
H-Index - 79
eISSN - 1467-9787
pISSN - 0022-4146
DOI - 10.1111/0022-4146.00216
Subject(s) - econometrics , spatial econometrics , heteroscedasticity , context (archaeology) , nonlinear system , spatial dependence , computer science , statistical hypothesis testing , econometric model , mathematics , statistics , geography , physics , archaeology , quantum mechanics
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.