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Evaluating the performance of AIC and BIC for selecting spatial econometric models

Author

Listed:
  • Christos Agiakloglou

    (University of Piraeus)

  • Apostolos Tsimpanos

    (University of the Aegean)

Abstract

This study investigates using a Monte Carlo analysis the performance of the two most important information criteria, such as the Akaike’s Information Criterion and the Bayesian Information Criterion, not only in terms of selecting the true spatial econometric model but also in term of detecting spatial dependence in comparison with the LM tests for the simple two spatial models SLM and SEM. The analysis is also extended by incorporating several other spatial econometric models, such as the SLX, SDM, SARAR and SDEM, along with heteroscedastic and non-normal errors. Simulation results show that under ideal conditions these criteria can assist the analyst to select the true spatial econometric model and detect properly spatial dependence.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:jospat:v:4:y:2023:i:1:d:10.1007_s43071-022-00030-x
    DOI: 10.1007/s43071-022-00030-x
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    References listed on IDEAS

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    1. Nicolas Debarsy & James P. LeSage, 2022. "Bayesian Model Averaging for Spatial Autoregressive Models Based on Convex Combinations of Different Types of Connectivity Matrices," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(2), pages 547-558, April.
    2. 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.
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    More about this item

    Keywords

    Spatial dependence; Spatial econometric models; LM tests; Information criteria; Monte Carlo analysis;
    All these keywords.

    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

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