Table of Contents

Spatial Dynamics, Networks and Modelling

Spatial Dynamics, Networks and Modelling

New Horizons in Regional Science series

Edited by Aura Reggiani and Peter Nijkamp

This important new book provides a valuable set of studies on spatial dynamics, emerging networks and modelling efforts. It employs interdisciplinary concepts alongside innovative trajectories to highlight recent advances in analysing and modelling the spatial economy, transport networks, industrial dynamics and regional systems. It is argued that modelling network processes at different spatial scales provides critical information for the design of plans and policies. Furthermore, a key issue in the current complex and heterogeneous landscape is the adoption and validation of new approaches, models and methodologies, which are able to grasp the emergent aspects of economic uncertainty and discontinuity, as well as overcome the current difficulties of carrying out appropriate forecasts. In exploring diverse pathways for theoretical, methodological and empirical analysis, this exciting volume offers promising and evolutionary perspectives on the modern spatial network society.

Chapter 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

Subjects: economics and finance, regional economics, urban and regional studies, regional economics


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 an exogenously provided 91 92 Analytical Advances in Modelling the Space-Economy weights matrix to specify...

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