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Simulation-based multiple testing for many non-nested multivariate models
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AI
DOI：10.1080/07474938.2026.2643748.png)
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Abstract
En 中文
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.
Keywords:
Consumption CAPM
Fama-French factors
liquidity factors
Monte Carlo tests
multivariate regression
non-nested hypotheses
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