Working Paper: CEPR ID: DP18407
Authors: Sushant Acharya; William Chen; Marco Del Negro; Keshav Dogra; Aidan Gleich; Shlok Goyal; Ethan Matlin; Donggyu Lee; Reca Sarfati; Sikata Sengupta
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
JEL Codes: C11; C32; D31; E32; E37; E52
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Central bank policies (E58) | Inequality (D63) |
| Inequality (D63) | Effectiveness of monetary policy (E52) |
| HANK model specification (C51) | Forecasting performance (C53) |
| HANK model forecasting accuracy (E17) | Consumption growth (E20) |
| Representative agent model forecasting accuracy (E17) | Consumption growth (E20) |