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Variable selection in STAR models with neighbourhood effects using genetic algorithms

Author

Listed:
  • Isolina Alberto
  • Asunción Beamonte
  • Pilar Gargallo
  • Pedro M. Mateo
  • Manuel Salvador

Abstract

In this paper we deal with the problem of variable selection in spatiotemporal autoregressive (STAR) models with neighbourhood effects. We propose a procedure to carry out the selection process, taking into account the uncertainty associated with estimation of the parameters and the predictive behaviour of the compared models, in order to give more realism to the analysis. We set up a multi-objective programming problem that combines the use of different criteria to measure both these aspects. We use genetic algorithms which are very flexible and suitable for our multicriteria decision problem. In particular, the procedure allows us to estimate the number of spatial and temporal nearest neighbours as well as their relative effects. The methodology is illustrated through an application to the real estate market of Zaragoza. Copyright (C) 2010 John Wiley & Son, Ltd.

Suggested Citation

  • Isolina Alberto & Asunción Beamonte & Pilar Gargallo & Pedro M. Mateo & Manuel Salvador, 2010. "Variable selection in STAR models with neighbourhood effects using genetic algorithms," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(8), pages 728-750, December.
    • Handle: RePEc:jof:jforec:v:29:y:2010:i:8:p:728-750
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    File URL: http://hdl.handle.net/10.1002/for.1164
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    Cited by:

    1. Thanos, Sotirios & Dubé, Jean & Legros, Diègo, 2016. "Putting time into space: the temporal coherence of spatial applications in the housing market," Regional Science and Urban Economics, Elsevier, vol. 58(C), pages 78-88.
    2. He Jiang, 2022. "A novel robust structural quadratic forecasting model and applications," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1156-1180, September.

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