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Heckelei, Thomas; Mittelhammer, Ron C.

Bayesian bootstrap multivariate regression. (English) Zbl 1026.62019

J. Econom. 112, No. 2, 241-264 (2003).
Summary: A Bayesian bootstrap multivariate regression (BBMR) procedure is presented that allows robust Bayesian analysis of multivariate regression models. BBMR does not require a parametric specification for the likelihood function and instead uses a bootstrapped likelihood based on the sampling distribution of location and scale estimators. A mixing algorithm for implementing the procedure automatically incorporates the scale invariant ignorance prior on the covariance matrix. BBMR can be implemented as a generic algorithm in standard statistical software independently of the actual choice of prior distributions. Monte Carlo evidence is provided showing accuracy and robustness of the approach in representing posterior distributions.

Cited in 2 Documents

MSC:

62F15 Bayesian inference
62P20 Applications of statistics to economics
62H12 Estimation in multivariate analysis
62F35 Robustness and adaptive procedures (parametric inference)
62J05 Linear regression; mixed models
62F40 Bootstrap, jackknife and other resampling methods

Keywords:

bootstrapping; robust likelihood; multivariate regression; simultaneous equation mappings
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References:

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