A Geometric Approach to Nonlinear Econometric Models
针对最小距离模型中结构参数与简化式参数关系高度非线性导致传统检验不可靠的问题，利用模型曲率推导有限样本界，构造了一致渐近有效的复合假设检验方法。
Conventional tests for composite hypotheses in minimum distance models can be unreliable when the relationship between the structural and reduced-form parameters is highly nonlinear.Such nonlinearity may arise for a variety of reasons, including weak identication.In this note we begin by studying the problem of testing a curved null in a nite-sample Gaussian model.Using the curvature of the model we develop new nite-sample bounds on the distribution of minimum-distance statistics.These bounds allow us to construct tests for composite hypotheses which are uniformly asymptotically valid over a large class of data generating processes and structural models.