Arbeitspapier
Online estimation of DSGE models
This paper illustrates the usefulness of sequential Monte Carlo (SMC) methods in approximating DSGE model posterior distributions. We show how the tempering schedule can be chosen adaptively, explore the benefits of an SMC variant we call generalized tempering for "online" estimation, and provide examples of multimodal posteriors that are well captured by SMC methods. We then use the online estimation of the DSGE model to compute pseudo-out-of-sample density forecasts of DSGE models with and without financial frictions and document the benefits of conditioning DSGE model forecasts on nowcasts of macroeconomic variables and interest rate expectations. We also study whether the predictive ability of DSGE models changes when we use priors that are substantially looser than those commonly adopted in the literature.
- Sprache
-
Englisch
- Erschienen in
-
Series: Staff Report ; No. 893
- 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
Forecasting Models; Simulation Methods
Business Fluctuations; Cycles
Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications
Monetary Policy
- Thema
-
adaptive algorithms
Bayesian inference
density forecasts
online estimation
sequential Monte Carlo methods
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Cai, Michael
Del Negro, Marco
Herbst, Edward P.
Matlin, Ethan
Sarfati, Reca
Schorfheide, Frank
- Ereignis
-
Veröffentlichung
- (wer)
-
Federal Reserve Bank of New York
- (wo)
-
New York, NY
- (wann)
-
2019
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
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Objekttyp
- Arbeitspapier
Beteiligte
- Cai, Michael
- Del Negro, Marco
- Herbst, Edward P.
- Matlin, Ethan
- Sarfati, Reca
- Schorfheide, Frank
- Federal Reserve Bank of New York
Entstanden
- 2019
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