Bayesian analysis of autoregressive panel data model: application in genetic evaluation of beef cattle

Abstract

The animal breeding values forecasting at futures times is a relevant technological innovation in the field of Animal Science, since its enables a previous indication of animals that will be either kept by the producer for breeding purposes or discarded. This study discusses an MCMC Bayesian methodology applied to panel data in a time series context. We consider Bayesian analysis of an autoregressive, AR(p), panel data model of order p, using an exact likelihood function, comparative analysis of prior distributions and predictive distributions of future observations. The methodology was tested by a simulation study using three priors: hierarchical Multivariate Normal-Inverse Gamma (model 1), independent Multivariate Student's t Inverse Gamma (model 2) and Jeffrey's (model 3). Comparisons by Pseudo-Bayes Factor favored model 2. The proposed methodology was applied to longitudinal data relative to Expected Progeny Difference (EPD) of beef cattle sires. The forecast efficiency was around 80%. Regarding the mean width of the EPD interval estimation (95%) in a future time, a great advantage was observed for the proposed Bayesian methodology over usual asymptotic frequentist method.