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

Konferenzbeitrag

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.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: 51st Congress of the European Regional Science Association: "New Challenges for European Regions and Urban Areas in a Globalised World", 30 August - 3 September 2011, Barcelona, Spain

Klassifikation und Themen

• Klassifikation: Wirtschaft

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Krisztin, Tamás ; Koch, Matthias
• Ereignis: Veröffentlichung
• (wer): European Regional Science Association (ERSA)
• (wo): Louvain-la-Neuve
• (wann): 2011

Weitere Informationen

• Handle: http://hdl.handle.net/10419/120320
• Letzte Aktualisierung: 10.03.2025, 11:45 MEZ