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Bayesian inference in regression with Pearson disturbances


  • Tsionas, Efthymios G.


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

    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.
    2. Jondeau, Eric & Rockinger, Michael, 2001. "Gram-Charlier densities," Journal of Economic Dynamics and Control, Elsevier, vol. 25(10), pages 1457-1483, October.
    3. Chib, Siddhartha & Greenberg, Edward, 1994. "Bayes inference in regression models with ARMA (p, q) errors," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 183-206.
    4. Gallant, A Ronald & Nychka, Douglas W, 1987. "Semi-nonparametric Maximum Likelihood Estimation," Econometrica, Econometric Society, vol. 55(2), pages 363-390, March.
    5. Lee, Tom K Y & Tse, Y K, 1991. "Term Structure of Interest Rates in the Singapore Asian Dollar Market," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 6(2), pages 143-152, April-Jun.
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    More about this item


    Pearson distributions; Likelihood function; Posterior distribution; MCMC; Bayesian inference;

    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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