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Efficient posterior integration in stable paretian models

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Abstract

The paper proposes a Markov Chain Monte Carlo method for Bayesian analysis of general regression models with disturbances from the family of stable distributions with arbitrary characteristic exponent and skewness parameter. The method does not require data augmentation and is based on combining fast Fourier transforms of the characteristic function to get the likelihood function and a Metropolis random walk chain to perform posterior analysis. Both a validation nonlinear regression and a nonlinear model for the Standard and Poor’s composite price index illustrate the method.

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Authors and Affiliations

  1. Council of Economic Advisers, Ministry of National Economy, 5 Nikis Street, Constitution Square, 10180, Athens, Greece

    Efthymios G. Tsonias

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  1. Efthymios G. Tsonias
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Tsonias, E.G. Efficient posterior integration in stable paretian models. Statistical Papers 41, 305–325 (2000). https://doi.org/10.1007/BF02925925

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  • DOI: https://doi.org/10.1007/BF02925925

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