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

Christos Agiakloglou and Apostolos Tsimpanos
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Apostolos Tsimpanos: University of the Aegean

Journal of Spatial Econometrics, 2023, vol. 4, issue 1, 1-35

Abstract: 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.

Keywords: Spatial dependence; Spatial econometric models; LM tests; Information criteria; Monte Carlo analysis (search for similar items in EconPapers)
JEL-codes: C20 C21 C52 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s43071-022-00030-x

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