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.
- Sprache
-
Englisch
- Erschienen in
-
Series: JRC Working Papers in Economics and Finance ; No. 2021/3
- Klassifikation
-
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
- Thema
-
DSGE
occasionally binding constraints
nonlinear estimation
Piecewise Kalman Filter
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Giovannini, Massimo
Pfeiffer, Philipp
Ratto, Marco
- Ereignis
-
Veröffentlichung
- (wer)
-
European Commission
- (wo)
-
Ispra
- (wann)
-
2021
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Giovannini, Massimo
- Pfeiffer, Philipp
- Ratto, Marco
- European Commission
Entstanden
- 2021
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