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Bayesian inference in regression with Pearson disturbances

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  • Tsionas, Efthymios G.

Abstract

In this paper we propose new estimation techniques in connection with regression models whose errors have distributions which are members of the celebrated Pearson’s system. Efficient MCMC procedures are proposed in the context of likelihood—based inference. The new techniques are applied to four major currencies.

Suggested Citation

  • Tsionas, Efthymios G., 2013. "Bayesian inference in regression with Pearson disturbances," Economics Letters, Elsevier, vol. 118(1), pages 177-181.
    • Handle: RePEc:eee:ecolet:v:118:y:2013:i:1:p:177-181
    DOI: 10.1016/j.econlet.2012.10.021
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    References listed on IDEAS

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    1. Hansen, Christian & McDonald, James B. & Newey, Whitney K., 2010. "Instrumental Variables Estimation With Flexible Distributions," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(1), pages 13-25.
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    More about this item

    Keywords

    Pearson distributions; Likelihood function; Posterior distribution; MCMC; Bayesian inference;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions

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