Robust Inference for Consumption‐Based Asset Pricing
传统资产定价测试在宏观风险因子（如消费增长）下不可靠，本文扩展了Gibbons-Ross-Shanken统计量来检验风险溢价识别，并构建95%置信集，发现当时间序列样本量远超资产数量时，平均收益不能被贝塔完全解释。
ABSTRACT The reliability of traditional asset pricing tests depends on: (i) the correlations between asset returns and factors; (ii) the time series sample size T compared to the number of assets N . For macro‐risk factors, like consumption growth, (i) and (ii) are often such that traditional tests cannot be trusted. We extend the Gibbons‐Ross‐Shanken statistic to test identification of risk premia and construct their 95% confidence sets. These sets are wide or unbounded when T and N are close, but show that average returns are not fully spanned by betas when T exceeds N considerably. Our findings indicate when meaningful empirical inference is feasible.