  We develop a generally applicable full-information inference method for
heterogeneous agent models, combining aggregate time series data and repeated
cross sections of micro data. To handle unobserved aggregate state variables
that affect cross-sectional distributions, we compute a numerically unbiased
estimate of the model-implied likelihood function. Employing the likelihood
estimate in a Markov Chain Monte Carlo algorithm, we obtain fully efficient and
valid Bayesian inference. Evaluation of the micro part of the likelihood lends
itself naturally to parallel computing. Numerical illustrations in models with
heterogeneous households or firms demonstrate that the proposed
full-information method substantially sharpens inference relative to using only
macro data, and for some parameters micro data is essential for identification.
