TITLE:
A Mixture-Based Bayesian Model Averaging Method
AUTHORS:
Georges Nguefack-Tsague, Walter Zucchini
KEYWORDS:
Mixture, Bayesian Model Selection, Bayesian Model Averaging, Bayesian Theory, Frequentist Performance
JOURNAL NAME:
Open Journal of Statistics,
Vol.6 No.2,
April
21,
2016
ABSTRACT: Bayesian model averaging (BMA) is a popular and powerful
statistical method of taking account of uncertainty about model form or
assumption. Usually the long run (frequentist) performances of the resulted
estimator are hard to derive. This paper proposes a mixture of priors and
sampling distributions as a basic of a Bayes estimator. The frequentist
properties of the new Bayes estimator are automatically derived from Bayesian
decision theory. It is shown that if all competing models have the same
parametric form, the new Bayes estimator reduces to BMA estimator. The method
is applied to the daily exchange rate Euro to US Dollar.