Robustness tests of the augmented Solow model
展示了横截面和面板数据回归的稳健性检验技术，并应用于扩展索洛增长模型，发现技术参数和收敛速度对测量误差高度敏感。
This paper demonstrates some techniques for testing the robustness of cross-section and panel data regressions, and applies them to the influential augmented Solow growth model. The paper focuses on robust estimation and analysis of sensitivity to measurement error. In particular, it is shown that estimated technology parameters and convergence rates are highly sensitive to measurement error. © 1998 John Wiley & Sons, Ltd.