Least squares estimation in nonstationary nonlinear cohort panels with learning from experience

TL;DR

This paper develops estimation techniques for nonstationary nonlinear cohort panels with learning, establishing their statistical properties and demonstrating their practical effectiveness through simulations and an empirical application.

Contribution

It introduces consistent and asymptotically normal nonlinear least squares estimators for nonstationary cohort panels with learning effects, addressing hypothesis testing challenges.

Findings

Estimator is consistent and asymptotically normal

Monte Carlo simulations confirm finite sample properties

Application to survey data demonstrates practical usefulness

Abstract

We discuss techniques of estimation and inference for nonstationary nonlinear cohort panels with learning from experience, showing, inter alia, the consistency and asymptotic normality of the nonlinear least squares estimator used in empirical practice. 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.