Estimating HANK for Central Banks

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

No 18407, CEPR Discussion Papers from C.E.P.R. Discussion Papers

Abstract: 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.

Keywords: Hank; Bayesian inference; Sequential monte carlo methods (search for similar items in EconPapers)
JEL-codes: C11 C32 D31 E32 E37 E52 (search for similar items in EconPapers)
Date: 2023-08
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Working Paper: Estimating HANK for Central Banks (2023)
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