Estimation of Heterogeneous Agent Models: A Likelihood Approach

Juan Parra-Alvarez, Olaf Posch and Mu-Chun Wang

No 6717, CESifo Working Paper Series from CESifo

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.

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)
Date: 2017
New Economics Papers: this item is included in nep-ecm, nep-mac and nep-ore
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Citations: View citations in EconPapers (6)

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Related works:
Working Paper: Estimation of heterogeneous agent models: A likelihood approach (2020)
Working Paper: Estimation of heterogeneous agent models: A likelihood approach (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:ces:ceswps:_6717

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