Online estimation of DSGE models

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Staff Report ; No. 893

Classification and Topics

• Classification: 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
• Subject: adaptive algorithms ; Bayesian inference ; density forecasts ; online estimation ; sequential Monte Carlo methods

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Cai, Michael ; Del Negro, Marco ; Herbst, Edward P. ; Matlin, Ethan ; Sarfati, Reca ; Schorfheide, Frank
• Event: Veröffentlichung
• (who): Federal Reserve Bank of New York
• (where): New York, NY
• (when): 2019

Further information

• Handle: http://hdl.handle.net/10419/210745
• Last update: 10.03.2025, 11:43 AM CET