A Bayesian Estimation of HANK models with Continuous Time Approach:Comparison between US and Japan

Ryo Hasumi and Hirokuni Iiboshi

MPRA Paper from University Library of Munich, Germany

Abstract: Abstract This paper estimates heterogeneous agent New Keynesian (HANK) model for US and Japan through three aggregate observations: real GDP, inflation and interest rate, by adopting combination of easy-to-use computational method for solving the model, developed by Ahn, Kaplan, Moll, Winberry and Wolf (2019), and sequential Monte Carlo (SMC) method with Kalman filter applied for Bayesian estimation with parallel computing. The combination make us enjoy the estimation of HANK just using a Laptop PC, e.g., Mac Book Pro, with MATLAB, neither many-core server computer nor FORTRUN language. We show estimation results of one Asset HANK model, i.e., impulse response, fluctuations of distributions of heterogeneous agent as well as historical decomposition for both countries. Even though using the same model, different data draws different pictures.

Keywords: Heterogeneous Agent model; Linearization; Model Reduction; Bayesian estimation; Sequential Monte Carlo; Kalman Filter (search for similar items in EconPapers)
JEL-codes: C32 E12 E21 E32 E43 E52 E62 (search for similar items in EconPapers)
Date: 2019-02-20
New Economics Papers: this item is included in nep-cmp, nep-mac and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:92292

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