Power-boosting in Specification Tests using Kernel Directional Component

We propose power-boosting strategies for kernel-based specification tests in conditional moment models, with a focus on the Kernel Conditional Moment (KCM) test. By decomposing the KCM statistic into spectral components, we demonstrate that truncating poorly estimated directions and selecting kernels based on a non-asymptotic signal-to-noise ratio significantly improves both test power and size control. Our theoretical and simulation results demonstrate that, while divergent component weights may offer higher asymptotic power, convergent component weights perform better in finite samples. The methods outperform existing tests across various settings and are illustrated in an empirical application.