Learning Before Testing: A Selective Nonparametric Test for Conditional Moment Restrictions
通过Lasso选择非参数级数回归中的近似函数，并考虑数据驱动选择的影响，构建了一个新的条件矩约束检验，其临界值基于非标准截断高斯渐近近似，在蒙特卡洛研究和通胀预测条件评估中表现出更好的适应性和功效。
Abstract We develop a new test for conditional moment restrictions via nonparametric series regression, with approximating functions selected by Lasso. A key novelty of our approach is to account for the effect of the data-driven selection, yielding a new critical value constructed on the basis of a nonstandard truncated-Gaussian asymptotic approximation. We show that the test is correctly sized and attains a well-defined sense of adaptiveness that may result in better power than existing methods. The improvement afforded by the new test is demonstrated in a Monte Carlo study and an empirical application on the conditional evaluation of inflation forecasts.