Bayesian inference in regression with Pearson disturbances
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.
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- Christian Hansen & James B. McDonald & Whitney Newey, 2007.
"Instrumental variables estimation with flexible distribution,"
CeMMAP working papers
CWP21/07, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- 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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- 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.
- 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 143-52, April-Jun.
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