This paper develops a methodology, building on a local-PCA approach, for inference on the pricing ability of conditional asset pricing models designed to mitigate the effect of omitted risk factors and misspecified conditional dynamics. The methodology is designed to exploit the rich information available in large cross-sections of individual stocks. Monte Carlo experiments and an empirical application demonstrate the benefits of this methodology over existing approaches.