Simulation-based multiple testing for many non-nested multivariate models
提出一种基于模拟的多元回归非嵌套假设检验方法，允许同时检验多个备择假设，在资产定价模型中发现流动性因子是拒绝原模型的关键因素。
We propose non-nested hypotheses tests in multivariate regressions. Our approach relies on regression augmentation, and allows for multiple alternatives. Tests are bootstrap-based, and exact under Gaussian disturbances. Simulations document good size and power properties for single and multiple alternatives. Tests are applied to asset pricing models with the Fama and French factors as the null hypothesis, and consumption-based and liquidity-augmented factors as the alternatives. Results reveal intermittent rejections over relatively short sub-samples at the quarterly frequency. The null model is rejected as a long run stable specification. Overall, the liquidity factor emerges as a key driver of such rejections.