We discuss techniques of estimation and inference for nonlinear cohort panels
with learning from experience, showing, inter alia, the consistency and
asymptotic normality of the nonlinear least squares estimator employed in the
seminal paper by Malmendier and Nagel (2016). Potential pitfalls for hypothesis
testing are identified and solutions proposed. Monte Carlo simulations verify
the properties of the estimator and corresponding test statistics in finite
samples, while an application to a panel of survey expectations demonstrates
the usefulness of the theory developed