Bootstrap Testing in Nonlinear Models
提出在非线性模型的自助法检验中，仅对每个自助样本执行少量牛顿或拟牛顿步骤，以降低计算成本，并在Tobit模型和共同因子约束检验中验证了方法的有效性。
Bootstrap testing of nonlinear models normally requires at least one nonlinear estimation for every bootstrap sample. We show how to reduce computational costs by performing only a fixed, small number of Newton or quasi‐Newton steps for each bootstrap sample. The number of steps is smaller for likelihood ratio tests than for other types of classical tests and smaller for Newton's method than for quasi‐Newton methods. The suggested procedures are applied to tests of slope coefficients in the tobit model and to tests of common factor restrictions. In both cases, bootstrap tests work well, and very few steps are needed.