Estimating HANK for central banks

Arbeitspapier

We provide a toolkit for efficient online estimation of heterogeneous agent (HA) New Keynesian (NK) models based on Sequential Monte Carlo methods. We use this toolkit to compare the out-of-sample forecasting accuracy of a prominent HANK model, Bayer et al. (2022), to that of the representative agent (RA) NK model of Smets and Wouters (2007, SW). We find that HANK's accuracy for real activity variables is notably inferior to that of SW. The results for consumption in particular are disappointing since the main difference between RANK and HANK is the replacement of the RA Euler equation with the aggregation of individual households' consumption policy functions, which reflects inequality.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: Staff Report ; No. 1071

Klassifikation und Themen

• 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 ; Personal Income, Wealth, and Their Distributions ; Business Fluctuations; Cycles ; Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications ; Monetary Policy
• Thema: HANK ; Bayesian inference ; sequential Monte Carlo methods

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Acharya, Sushant ; Chen, William ; Del Negro, Marco ; Dogra, Keshav ; Gleich, Aidan ; Goyal, Shlok ; Matlin, Ethan ; Lee, Donggyu ; Sarfati, Reca ; Sengupta, Sikata
• Ereignis: Veröffentlichung
• (wer): Federal Reserve Bank of New York
• (wo): New York, NY
• (wann): 2023

Weitere Informationen

• DOI: doi:10.59576/sr.1071
• Handle: http://hdl.handle.net/10419/284031
• Letzte Aktualisierung: 10.03.2025, 11:41 MEZ