Dynamic Bayesian beta models

0502 economics and business 05 social sciences 0101 mathematics 01 natural sciences
DOI: 10.1016/j.csda.2010.12.011 Publication Date: 2011-01-06T09:29:01Z
ABSTRACT
We develop a dynamic Bayesian beta model for modeling and forecasting single time series of rates or proportions. This work is related to a class of dynamic generalized linear models (DGLMs), although, for convenience, we use non-conjugate priors. The proposed methodology is based on approximate analysis relying on Bayesian linear estimation, nonlinear system of equations solution and Gaussian quadrature. Intentionally we avoid MCMC strategy, keeping the desired sequential nature of the Bayesian analysis. Applications to both real and simulated data are provided.
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