Posterior and Predictive Densities for Nonlinear Regression: A Partly Linear Model Case

摘要

The paper continues the Bayesian analysis of nonlinear regression models, that is models of known functional form (nonlinear in parameters) and with an additive error term. The paper generalizes the approach adopted previously for the CES functions and deals with Bayesian estimation and prediction for those nonlinear regression models which are linear in some parameters given values of the remaining parameters. This class of nonlinear regression models is worth considering since the exact Bayesian analysis with an appropriately chosen prior requires only q- or (q+1)-dimensional numerical integrations, irrespective of k.