Staff Reports
Estimating HANK for Central Banks
Number 1071
August 2023

JEL classification: C11, C32, D31, E32, E37, E52

Authors: Sushant Acharya, William Chen, Marco Del Negro, Keshav Dogra, Aidan Gleich, Shlok Goyal, Ethan Matlin, Donggyu Lee, Reca Sarfati, and Sikata Sengupta

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.

Full Article
Author Disclosure Statement(s)
Sushant Acharya, William Chen, Marco Del Negro, Keshav Dogra, Aidan Gleich, Shlok Goyal, Ethan Matlin, Donggyu Lee, Reca Sarfati, Sikata Sengupta
We declare that we have no relevant or material financial interests that relate to the research described in this paper. Some of us work, or worked during the completion of this project, at a central bank, such as the Bank of Canada (Acharya) or the Federal Reserve Bank of New York (Del Negro, Dogra, Gleich, Lee, Sengupta). Those of us who did not work at a Central Bank (Chen, Goyal, Matlin, Sarfati) received an honorarium for the completion of the project that only tied to its quality but not its message.
Suggested Citation:
Acharya, Sushant, William Chen, Marco Del Negro, Keshav Dogra, Aidan Gleich, Shlok Goyal, Ethan Matlin, Donggyu Lee, Reca Sarfati, and Sikata Sengupta. 2023. “Estimating HANK for Central Banks.” Federal Reserve Bank of New York Staff Reports, no. 1071, August. https://doi.org/10.59576/sr.1071

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