Estimation of Dynamic Models with Occasionally Binding Constraints.

Abstract

We present an algorithm for estimating non-linear dynamic models, including those featuring occasionally binding constraints. The algorithm extends the Cubature Kalman Filter of Arasaratnam and Haykin (2009) with dynamic state space reduction, to give adequate speed in the presence of occasionally binding constraints, and to ensure that it can handle the large state spaces generated by pruned perturbation solutions to medium-scale DSGE models. We further extend the base algorithm to allow for alternative cubature procedures to improve the tracking of non-linearities. The algorithm relies on the solution method for models with occasionally binding constraints of Holden (2016b). We illustrate that the method can solve some of the identification problems that plague linearized DSGE models.