Bayesian estimation and forecasting in nonlinear models : application to an LSTAR model
Résumé
This paper considers the Bayesian estimation and prediction in a non-linear model by means of Monte Carlo integration with importance sampling. The importance function is derived from a first-order Taylor series expansion of the non-linear conditional expectation of the endogenous variable. The method is applied to an LSTAR model with an artificial sample.