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Bayesian local influence analysis: With an application to stochastic frontiers

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  • Tsionas, Mike G.

Abstract

A Bayesian alternative to Zhuo (2018) is presented. The method is of general interest as it presents an explicit formula for the local sensitivity of log marginal likelihood when observations vary by a small amount. The remarkable feature is that the formula is very easy to compute and does not require knowledge of the marginal likelihood which is, invariably, extremely difficult to compute. Similar expressions are derived for posterior moments and other functions of interest, including inefficiency. Methods for examining prior sensitivity in a straightforward way are also presented. The methods are illustrated in the context of a stochastic production frontier.

Suggested Citation

  • Tsionas, Mike G., 2018. "Bayesian local influence analysis: With an application to stochastic frontiers," Economics Letters, Elsevier, vol. 165(C), pages 54-57.
    • Handle: RePEc:eee:ecolet:v:165:y:2018:i:c:p:54-57
    DOI: 10.1016/j.econlet.2018.02.005
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    References listed on IDEAS

    as
    1. Hongtu Zhu & Joseph G. Ibrahim & Niansheng Tang, 2011. "Bayesian influence analysis: a geometric approach," Biometrika, Biometrika Trust, vol. 98(2), pages 307-323.
    2. Zhuo, Shuaihe, 2018. "Local influence analysis of stochastic frontier estimation: A case-weights perturbation approach," Economics Letters, Elsevier, vol. 164(C), pages 79-81.
    3. Fernandez, Carmen & Osiewalski, Jacek & Steel, Mark F. J., 1997. "On the use of panel data in stochastic frontier models with improper priors," Journal of Econometrics, Elsevier, vol. 79(1), pages 169-193, July.
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    Cited by:

    1. Kamil Makieła & Błażej Mazur, 2022. "Model uncertainty and efficiency measurement in stochastic frontier analysis with generalized errors," Journal of Productivity Analysis, Springer, vol. 58(1), pages 35-54, August.

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    More about this item

    Keywords

    Local influence; Bayesian analysis; Marginal likelihood; Posterior moments; Markov Chain Monte Carlo;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

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