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Cited by 450

The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.

Power Enhancement in High-Dimensional Cross-Sectional Tests

Open access
Fan, Jianqing, Liao, Yuan, Yao, Jiawei·2015·Econometrica
DOI 10.3982/ecta12749
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Abstract

= 0 against sparse alternatives where the null hypothesis is violated only by a couple of 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. As specific applications, the proposed methods are applied to testing the factor pricing models and validating the cross-sectional independence in panel data models.

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Metadata

Authors
Fan, J., Liao, Y., & Yao, J.
Venue
Econometrica
Publisher
The Econometric Society
Published
2015
Volume
83
Issue
4
Pages
1497-1541
ISSN
0012-9682 (ISSN-L)
DOI
10.3982/ecta12749
OpenAlex
W2123735241
Cited by
450
References
34
FWCI
6.72
Type
journal article
Language
en
Open access
green

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