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Multivariate student-T regression models : pitfalls and inference

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Author Info
Fernandez, C.
Steel, M.F.J. (Tilburg University, Center for Economic Research)

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Abstract

We consider likelihood-based inference from multivariate regression models with in- dependent Student-t errors. Some very intruiging pitfalls of both Bayesian and classical methods on the basis of point observations are uncovered. Bayesian inference may be precluded as a consequence of the coarse nature of the data. Global maximization of the likelihood function is a vacuous exercise since the likelihood function is unbounded as we tend to the boundary of the parameter space. A Bayesian analysis on the basis of set observations is proposed and illustrated by several examples.

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Paper provided by Tilburg University, Center for Economic Research in its series Discussion Paper with number 8.

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Date of creation: 1997
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Handle: RePEc:dgr:kubcen:19978

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Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

  1. John Geweke, 1992. "Priors for macroeconomic time series and their application," Discussion Paper / Institute for Empirical Macroeconomics 64, Federal Reserve Bank of Minneapolis. [Downloadable!]
    Other versions:
  2. Fernandez, C. & Steel, M.F.J., 1997. "On the dangers of modelling through continuous distributions : a Bayesian perspective," Discussion Paper 5, Tilburg University, Center for Economic Research. [Downloadable!]
    Other versions:
  3. repec:cup:etheor:v:10:y:1994:i:3-4:p:609-32 is not listed on IDEAS
  4. Liu, C., 1995. "Missing Data Imputation Using the Multivariate t Distribution," Journal of Multivariate Analysis, Elsevier, vol. 53(1), pages 139-158, April. [Downloadable!] (restricted)
  5. Geweke, J, 1993. "Bayesian Treatment of the Independent Student- t Linear Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(S), pages S19-40, Suppl. De. [Downloadable!] (restricted)
  6. Fernandez, C. & Steel, M.F.J., 1996. "On Bayesian modelling of fat tails and skewness," Discussion Paper 58, Tilburg University, Center for Economic Research. [Downloadable!]
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(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Filidor Labra & Reiko Aoki & Heleno Bolfarine, 2005. "Local influence in null intercept measurement error regression under a student_t model," Journal of Applied Statistics, Taylor and Francis Journals, vol. 32(7), pages 723-740, September. [Downloadable!] (restricted)
  2. David Cademartori & Cecilia Romo & Ricardo Campos & Manuel Galea, 2003. "Robust estimation of systematic risk using the t distribution in the chilean stock markets," Applied Economics Letters, Taylor and Francis Journals, vol. 10(7), pages 447-453, May. [Downloadable!] (restricted)
  3. Jose T.A.S. Ferreira & Mark F.J. Steel, 2004. "Bayesian Multivariate Regression Analysis with a New Class of Skewed Distributions," Econometrics 0403001, EconWPA. [Downloadable!]
  4. Antonio Sanhueza & Víctor Leiva & N. Balakrishnan, 2008. "A new class of inverse Gaussian type distributions," Metrika, Springer, vol. 68(1), pages 31-49, June. [Downloadable!] (restricted)
  5. James E. Griffin & Mark F.J. Steel, 2002. "Inference With Non-Gaussian Ornstein-Uhlenbeck Processes for Stochastic Volatility," Econometrics 0201002, EconWPA, revised 04 Apr 2003. [Downloadable!]
    Other versions:
  6. Manuel Galea & Heleno Bolfarine & Filidor Vilcalabra, 2002. "Influence diagnostics for the structural errors-in-variables model under the Student-t distribution," Journal of Applied Statistics, Taylor and Francis Journals, vol. 29(8), pages 1191-1204, November. [Downloadable!] (restricted)
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