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Bayesian Local Influence for Spatial Autoregressive Models with Heteroscedasticity

Author

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  • Xiaowen Dai

    (Shanghai Lixin University of Accounting and Finance)

  • Libin Jin

    (Shanghai Lixin University of Accounting and Finance)

  • Maozai Tian

    (Renmin University of China)

  • Lei Shi

    (Yunnan University of Finance and Economics)

Abstract

This paper studies Bayesian local influence analysis for the spatial autoregressive models with heteroscedasticity (heteroscedastic SAR models). Two local diagnostic procedures using curvature-based and slope-based methods are proposed in the framework of Bayesian perspective. The curvature-based diagnostic are obtained by maximizing the normal curvature of an influence graph based on Kullback–Leibler divergence measure and slope-based diagnostic use the first order derivative of Bayesian factor defined for perturbation. Three perturbation schemes under the heteroscedastic SAR models are suggested and the diagnostic measures are derived respectively. The computations for the proposed diagnostic measures can be easily obtained using Markov Chain Monte Carlo sampler. The proposed methodologies are illustrated using two real examples.

Suggested Citation

  • Xiaowen Dai & Libin Jin & Maozai Tian & Lei Shi, 2019. "Bayesian Local Influence for Spatial Autoregressive Models with Heteroscedasticity," Statistical Papers, Springer, vol. 60(5), pages 1423-1446, October.
    • Handle: RePEc:spr:stpapr:v:60:y:2019:i:5:d:10.1007_s00362-017-0880-1
    DOI: 10.1007/s00362-017-0880-1
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