Arbeitspapier
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.
- Language
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Englisch
- Bibliographic citation
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Series: JRC Working Papers in Economics and Finance ; No. 2021/3
- Classification
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Wirtschaft
Bayesian Analysis: General
Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
Model Construction and Estimation
- Subject
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DSGE
occasionally binding constraints
nonlinear estimation
Piecewise Kalman Filter
- Event
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Geistige Schöpfung
- (who)
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Giovannini, Massimo
Pfeiffer, Philipp
Ratto, Marco
- Event
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Veröffentlichung
- (who)
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European Commission
- (where)
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Ispra
- (when)
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2021
- Handle
- Last update
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10.03.2025, 11:43 AM CET
Data provider
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ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Giovannini, Massimo
- Pfeiffer, Philipp
- Ratto, Marco
- European Commission
Time of origin
- 2021
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