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Evaluating information criteria for selecting spatial processes

Author

Listed:
  • Christos Agiakloglou

    (University of Piraeus)

  • Apostolos Tsimpanos

    (University of the Aegean)

Abstract

Information criteria have been widely used in many quantitative applications as an effort to select the most appropriate model that describes well enough the unknown population behavior for a given dataset. Studies have shown that their performance depends on several elements and the selection of the best fitted model is not always the same for all criteria. For this purpose, this research evaluates the performance of the three most often used information criteria, such as the Akaike information criterion, the Bayesian information criterion and Hannan and Quinn information criterion, for selecting spatial processes, taking into account that the sample in spatial analysis is regarded as a realization of a spatial process that incorporates the spatial dependence between the observations. Using a Monte Carlo analysis for the three most frequently applied in practice spatial processes, such as the first-order spatial autoregressive process, SAR(1), the first-order spatial moving average process, SMA(1), and the mixed spatial autoregressive moving average process, SARMA(1, 1), this study finds that these information criteria can assist the analyst to select the true process, but their behavior depends on sample size as well as on the magnitude of the spatial parameters, leading occasionally to alternative competitive processes.

Suggested Citation

  • Christos Agiakloglou & Apostolos Tsimpanos, 2021. "Evaluating information criteria for selecting spatial processes," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 66(3), pages 677-697, June.
    • Handle: RePEc:spr:anresc:v:66:y:2021:i:3:d:10.1007_s00168-020-01033-y
    DOI: 10.1007/s00168-020-01033-y
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    References listed on IDEAS

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    1. Jesús Mur & Ana Angulo, 2007. "Clues for discriminating between moving average and autoregressive models in spatial processes," Spanish Economic Review, Springer;Spanish Economic Association, vol. 9(4), pages 273-298, December.
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    Cited by:

    1. Ekinci, Ekin & İlhan Omurca, Sevinç & Özbay, Bilge, 2021. "Comparative assessment of modeling deep learning networks for modeling ground-level ozone concentrations of pandemic lock-down period," Ecological Modelling, Elsevier, vol. 457(C).
    2. Christos Agiakloglou & Apostolos Tsimpanos, 2023. "Evaluating the performance of AIC and BIC for selecting spatial econometric models," Journal of Spatial Econometrics, Springer, vol. 4(1), pages 1-35, December.

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    More about this item

    JEL classification:

    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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