Efficient and robust inference of models with occasionally binding constraints - Massimo Giovannini; Philipp Pfeiffer; Marco Ratto (European Commission, 2021)
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.
Keywords: C11; C32; C51; 330; DSGE; occasionally binding constraints; nonlinear estimation; Piecewise Kalman Filter