Nonlinear Panel Data Methods for Dynamic Heterogeneous Agent Models

Recent developments in nonlinear panel data analysis allow the identification and estimation of general dynamic systems. We review some results and techniques for nonparametric identification and flexible estimation in the presence of time-invariant and time-varying latent variables. This opens up the possibility of estimating nonlinear reduced forms in a large class of structural dynamic models with heterogeneous agents. We show how such reduced forms may be used to document policy-relevant derivative effects and to improve the understanding and implementation of structural models.