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.

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Marco Ratto (European Commission)

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IWH, conference room and via Zoom

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Marco Ratto is senior scientist at the European Commission’s Joint Research Center in Ispra, Italy, where he leads the project MACFIS (Macro-economic and fiscal surveillance). He studied engineering at the University of Genoa and obtained his Ph.D in Engineering in 1998.

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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.