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Modified harmonic mean method for spatial autoregressive models

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  • Doğan, Osman

Abstract

In this paper, I suggest using the modified harmonic mean method for estimating marginal likelihood functions of cross-sectional spatial autoregressive models. In a Bayesian estimation setting, I show how this method can be used for popular cross-sectional spatial autoregressive models. In a simulation study, I investigate the finite sample performance of this estimator along with some other popular information criteria for the nested and non-nested model selection problems. The simulation results show that the modified harmonic mean estimator performs satisfactorily, and can be useful for the specification search exercises in spatial econometrics.

Suggested Citation

  • Doğan, Osman, 2023. "Modified harmonic mean method for spatial autoregressive models," Economics Letters, Elsevier, vol. 223(C).
    • Handle: RePEc:eee:ecolet:v:223:y:2023:i:c:s0165176523000034
    DOI: 10.1016/j.econlet.2023.110978
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    References listed on IDEAS

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    5. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    6. Joshua C. C. Chan & Angelia L. Grant, 2016. "On the Observed-Data Deviance Information Criterion for Volatility Modeling," Journal of Financial Econometrics, Oxford University Press, vol. 14(4), pages 772-802.
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    More about this item

    Keywords

    Marginal likelihood; Modified harmonic mean; SAR; AIC; DIC; BIC; Model selection;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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