Least Squares Estimation in Nonstationary Nonlinear Cohort Panels with Learning from Experience
研究了非平稳非线性队列面板中基于经验学习的估计与推断方法，证明了非线性最小二乘估计量的一致性和渐近正态性，识别了假设检验的潜在陷阱并提出解决方案，蒙特卡洛模拟验证了有限样本性质，调查预期面板应用展示了理论实用性。
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.