Efficient and robust inference of models with occasionally binding constraints

This paper proposes a piecewise-linear Kalman filter (PKF) to estimate DSGE models with occasionally binding constraints. This method expands the set of models suitable for nonlinear estimation. It straightforwardly handles missing data, non-singularity (more shocks than observed time series), and large-scale models. We provide several applications to highlight its efficiency and robustness compared to existing methods. Our toolkit integrates the PKF into Dynare, the most popular software in DSGE modeling.