Bayesian Analysis of Nonlinear Time Series Models with a Threshold

This paper considers the Bayesian analysis of threshold regression models. It shows that this analysis can be conducted with simple deterministic numerical integration rules of low dimension. The shape of the posterior density is greatly determined by the type of threshold and of transition function considered. Unequal variances between the regimes usually adds one dimension to the integration problem, except in some cases where a simplification occurs.