Estimation of heterogeneous agent models: A likelihood approach

Publikation: Working paperForskning


  • rp20_05

    Forlagets udgivne version, 912 KB, PDF-dokument

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.
UdgiverInstitut for Økonomi, Aarhus Universitet
Antal sider33
StatusUdgivet - maj 2020
SerietitelCREATES Research Papers


  • Heterogeneous agent models, Continuous-time, Fokker-Planck equations, Kullback-Leibler divergence, Maximum likelihood

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