Template-Type: ReDIF-Paper 1.0
Author-Name: Hanno Kase
Author-X-Name-First: Hanno
Author-X-Name-Last: Kase
Author-Name: Leonardo Melosi
Author-X-Name-First: Leonardo
Author-X-Name-Last: Melosi
Author-Name: Matthias Rottner
Author-X-Name-First: Matthias
Author-X-Name-Last: Rottner
Title: Estimating nonlinear heterogeneous agent models with neural networks
Abstract: We leverage recent advancements in machine learning to develop an integrated method to solve globally and estimate models featuring agent heterogeneity, nonlinear constraints, and aggregate uncertainty. Using simulated data, we show that the proposed method accurately estimates the parameters of a nonlinear Heterogeneous Agent New Keynesian (HANK) model with a zero lower bound (ZLB) constraint. We further apply our method to estimate this HANK model using U.S. data. In the estimated model, the interaction between the ZLB constraint and idiosyncratic income risks emerges as a key source of aggregate output volatility.
Creation-Date: 2025-01
File-URL: https://www.bis.org/publ/work1241.htm
File-Format: Application/pdf
File-Function: Full PDF document
File-URL: https://www.bis.org/publ/work1241.htm
File-Format: text/html
Number: 1241
Keywords: neural networks, likelihood, global solution, heterogeneous agents, nonlinearity, aggregate uncertainty, HANK, zero lower bound
Classification-JEL: C11, C45, D31, E32, E52
Handle: RePEc:bis:biswps:1241