期刊
ECONOMETRICA
卷 83, 期 4, 页码 1497-1541出版社
WILEY
DOI: 10.3982/ECTA12749
关键词
Sparse alternatives; thresholding; large covariance matrix estimation; Wald test; screening; cross-sectional dependence; factor pricing model
类别
资金
- National Science Foundation [DMS-1206464, DMS-1406266]
- National Institute of Health [R01GM100474-01, R01-GM072611]
- University of Maryland
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [1206464] Funding Source: National Science Foundation
We propose a novel technique to boost the power of testing a high-dimensional vector H:=0 against sparse alternatives where the null hypothesis is violated by only a few components. Existing tests based on quadratic forms such as the Wald statistic often suffer from low powers due to the accumulation of errors in estimating high-dimensional parameters. More powerful tests for sparse alternatives such as thresholding and extreme value tests, on the other hand, require either stringent conditions or bootstrap to derive the null distribution and often suffer from size distortions due to the slow convergence. Based on a screening technique, we introduce a power enhancement component, which is zero under the null hypothesis with high probability, but diverges quickly under sparse alternatives. The proposed test statistic combines the power enhancement component with an asymptotically pivotal statistic, and strengthens the power under sparse alternatives. The null distribution does not require stringent regularity conditions, and is completely determined by that of the pivotal statistic. The proposed methods are then applied to testing the factor pricing models and validating the cross-sectional independence in panel data models.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据