Estimation of Heterogeneous Agent Models: A Likelihood Approach

Abstract

We study the statistical properties of heterogeneous agent models. Using a Bewley-Hugget-Aiyagari model we compute the density function of wealth and income and use it for likelihood inference. We study the finite sample properties of the maximum likelihood estimator (MLE) using Monte Carlo experiments on artificial cross-sections of wealth and income. We propose to use the Kullback-Leibler divergence to investigate identification problems that may affect inference. Our results suggest that the unrestricted MLE leads to considerable biases of some parameters. Calibrating weakly identified parameters allows to pin down the other unidentified parameter without compromising the estimation of the remaining parameters. We illustrate our approach by estimating the model for the U.S. economy using wealth and income data from the Survey of Consumer Finances.

Estimation of Heterogeneous Agent Models: A Likelihood Approach

“…An earlier version of this paper shows that the multimodality in these parameters' density remains even in large samples (see,Parra-Alvarez et al, 2017).…”

Full‐information estimation of heterogeneous agent models using macro and micro data

“…We are not aware of other papers that tackle the fundamental problem that the aggregate shocks affecting cross‐sectional heterogeneity are not directly observed. Parra‐Alvarez, Posch, and Wang (2020) use the model‐implied steady‐state micro likelihood in a heterogeneous household model, but abstract from macro data or aggregate dynamics. Closest to our approach are Fernández‐Villaverde, Hurtado, and Nuño (2019), who exploit the model‐implied joint sampling density of macro and micro data in a particular heterogeneous agent macro model.…”

Markov distributional equilibrium dynamics in games with complementarities and no aggregate risk

“…77 Equilibrium distributions are also important in econometric evaluations of heterogeneous agent models in macroeconomics. See, for example, Parra‐Alvarez, Posch, and Wang (2020) and Auclert, Bardoczy, Rognlie, and Straub (2021). …”