Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/172993
Authors: 
Parra-Alvarez, Juan Carlos
Posch, Olaf
Wang, Mu-Chun
Year of Publication: 
2017
Series/Report no.: 
CESifo Working Paper No. 6717
Abstract: 
In this paper, we study the statistical properties of heterogeneous agent models with incomplete markets. Using a Bewley-Hugget-Aiyagari model we compute the equilibrium density function of wealth and show how it can be used for likelihood inference. We investigate the identifiability of the model parameters based on data representing a large cross-section of individual wealth. We also study the finite sample properties of the maximum likelihood estimator using Monte Carlo experiments. Our results suggest that while the parameters related to the household’s preferences can be correctly identified and accurately estimated, the parameters associated with the supply side of the economy cannot be separately identified leading to inferential problems that persist even in large samples. In the presence of partially identification problems, we show that an empirical strategy based on fixing the value of one the troublesome parameters allows us to pin down the other unidentified parameter without compromising the estimation of the remaining parameters of the model. An empirical illustration of our maximum likelihood framework using the 2013 SCF data for the U.S. confirms the results from our identification experiments.
Subjects: 
heterogeneous agent models
continuous-time
Fokker-Planck equations
identification
maximum likelihood
JEL: 
C10
C13
C63
E21
E24
Document Type: 
Working Paper

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