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Agent Teams and Evolutionary Computation: Optimizing Semi- Parametric Spatial Autoregressive Models

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  • Tamás Krisztin
  • Matthias Koch

Abstract

Classical spatial autoregressive models share the same weakness as the classical linear regression models, namely it is not possible to estimate non-linear relationships between the dependent and independent variables. In the case of classical linear regression a semi-parametric approach can be used to address this issue. Therefore an advanced semi- parametric modelling approach for spatial autoregressive models is introduced. Advanced semi-parametric modelling requires determining the best configuration of independent variable vectors, number of spline-knots and their positions. To solve this combinatorial optimization problem an asynchronous multi-agent system based on genetic-algorithms is utilized. Three teams of agents work each on a subset of the problem and cooperate through sharing their most optimal solutions. Through this system more complex relationships between the dependent and independent variables can be derived. These could be better suited for the possibly non-linear real-world problems faced by applied spatial econometricians.

Suggested Citation

  • Tamás Krisztin & Matthias Koch, 2011. "Agent Teams and Evolutionary Computation: Optimizing Semi- Parametric Spatial Autoregressive Models," ERSA conference papers ersa11p1687, European Regional Science Association.
    • Handle: RePEc:wiw:wiwrsa:ersa11p1687
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    Cited by:

    1. Stilianos Alexiadis & Matthias Koch & Tamás Krisztin, 2011. "Time series and spatial interaction: An alternative method to detect converging clusters," ERSA conference papers ersa11p1678, European Regional Science Association.

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