Estimation of heterogeneous agent models: A likelihood approach - Juan Carlos Parra-Alvarez; Olaf Posch; Mu-Chun Wang (Deutsche Bundesbank, 2020)
We study the statistical properties of heterogeneous agent models. Using aBewley-Hugget-Aiyagari model we compute the density function of wealth and in-come and use it for likelihood inference. We study the finite sample properties of themaximum likelihood estimator (MLE) using Monte Carlo experiments on artificialcross-sections of wealth and income. We propose to use the Kullback-Leibler diver-gence to investigate identification problems that may affect inference. Our resultssuggest that the unrestricted MLE leads to considerable biases of some parameters.Calibrating weakly identified parameters allows to pin down the other unidentifiedparameter without compromising the estimation of the remaining parameters. Weillustrate our approach by estimating the model for the U.S. economy using wealthand income data from the Survey of Consumer Finances.
Keywords: C10; C13; C63; E21; E24; 330; Heterogeneous agent models; Continuous-time; Fokker-Planck equations; Kullback-Leibler divergence; Maximum likelihood