Authors: Sushant Acharya, William Chen, Marco Del Negro, Keshav Dogra, Aidan Gleich, Shlok Goyal, Ethan Matlin, Donggyu Lee, Reca Sarfati, and Sikata Sengupta
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Staff Reports
Estimating HANK for Central Banks
Number 1071
August 2023
August 2023
JEL classification: C11, C32, D31, E32, E37, E52
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.
Author Disclosure Statement(s)
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
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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