Estimation of heterogeneous agent models: A likelihood approach
Juan Parra-Alvarez (),
Olaf Posch () and
CREATES Research Papers from Department of Economics and Business Economics, Aarhus University
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.
Keywords: Heterogeneous agent models; Continuous-time; Fokker-Planck equations; Kullback-Leibler divergence; Maximum likelihood (search for similar items in EconPapers)
JEL-codes: C10 C13 C63 E21 E24 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-mac and nep-ore
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Working Paper: Estimation of heterogeneous agent models: A likelihood approach (2020)
Working Paper: Estimation of Heterogeneous Agent Models: A Likelihood Approach (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:aah:create:2020-05
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