- New Horizons in Regional Science series
Edited by Aura Reggiani and Peter Nijkamp
Chapter 5: Spatial Effects and Non-Linearity in Spatial Regression Models: Simulation Results for Several Misspecification Tests
5. Spatial Effects and Non-Linearity in Spatial Regression Models: Simulation Results for Several Misspecification Tests Thomas de Graaff, Kees van Montfort and Peter Nijkamp 5.1 INTRODUCTION In research on non-linear phenomena, two distinct approaches can be distinguished. The first approach is geared towards the improvement of model specifications in order to find convincing economic illustrations of the presence of non-linear features in real-world economic processes. The second approach is data instigated and centres on investigating time series for the presence of non-linearities. In regional science, Puu (1989), Dendrinos and Sonis (1990), and Nijkamp and Reggiani (1998) focus on non-linearity – and even on chaos – although largely from a theoretical point of view. The modelling approach to nonlinearity is hence represented in regional science, but the data-oriented approach is largely absent. In part, this is caused by a lack of interest in the specification and estimation of non-linear spatial models in spatial econometrics. This is vastly different from the situation in mainstream timeseries econometrics. Brock and Malliaris (1989), Rosser (1991), and Johnson and McClelland (1998) are key references from a substantial literature focusing on the detection and specification of non-linear dynamic processes. The focus of spatial econometrics has been the development of tests for spatial dependence or autocorrelation and spatial heterogeneity for what are called spatial process models (Anselin et al. 1996, provide an overview). In spatial process models, distance decay and spatial clustering patterns are not modelled through a distance deterrence function, but specified as autoregressive or moving average processes using...
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