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多弱工具变量模型的自助法推断AI 解读

This study’s main contribution is to theoretically analyze the application of bootstrap methods to instrumental variable models when the available instruments may be weak and the number of instruments goes to infinity with the sample size. We demonstrate that a standard residual-based bootstrap procedure cannot consistently estimate the distribution of the limited information maximum likelihood estimator or Fuller (1977) estimator under many/many weak instrument sequence.…

个体固定效应线性动态面板模型的自助法推断AI 解读

This paper’s main contribution is to propose and theoretically justify the application of bootstrap methods for inference in autoregressive panel data models with fixed effects. Whereas the focus of the existing literature has been on bias correcting the standard fixed effects OLS estimator (due to the well known incidental parameter bias), our focus here is on improving the quality of inference by relying on the bootstrap instead of the standard normal distribution when…