Testing the Diagonality of a Large Covariance Matrix in a Regression Setting

Lan, Wei; Luo, Ronghua; Tsai, Chih‐Ling; Wang, Hansheng; Yang, Yunhong

doi:10.1080/07350015.2014.923317

Cited by 10 publications

(4 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…() and Lan et al. () have developed useful tests for H 0 under simultaneous limits. Without these restrictions, to test H 0 , Pesaran () proposed the CD test, and Pesaran et al.…”

Section: Model and Related Testsmentioning

confidence: 99%

“…(, ), Mao (, ) and Lan et al. () for other examples. Roughly speaking, the role of the normality assumption is to ensure that we can directly test H 0 under simultaneous limits or HDLSS limits if the errors can be observed.…”

Section: Tests Based On Pairwise Augmented Regressionsmentioning

confidence: 99%

“…The other category is unstructured, which does not impose any prior dependence structure on the errors. Tests that fall into this category include the cross‐sectional dependence (CD) test (Pesaran, ) and the bias‐adjusted Lagrange multiplier (LM adj ) test (Pesaran et al., ) for the static linear heterogeneous panel data model, the bias‐corrected LM test (Baltagi et al., ) and the bias‐correction‐free LM test (Mao, ) for the one‐way fixed effects panel data model, and the bias‐corrected (BC) and adjusted Schott (AS) tests (Lan et al., ) for the seemingly unrelated regression equations (SURE) model . All these tests postulate that both the cross‐sectional dimension N and the time‐series dimension T of the panel data tend to infinity when approximating the distributions of the related statistics.…”

Section: Introductionmentioning

confidence: 99%

“…(), the bias‐correction‐free LM test in Mao () and the AS test in Lan et al. () assume

N / T \to c

additionally as

(N, T) \to \infty

, where c is a fixed and finite positive number. Unlike these cases, the newly developed tests in the present paper do not need any restriction imposed on N and T .…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Testing for error cross‐sectional independence using pairwise augmented regressions

Mao

2016

The Econometrics Journal

View full text Add to dashboard Cite

Summary This paper proposes two statistics for testing error cross‐sectional independence in a static linear heterogeneous panel data model by virtue of pairwise augmented regressions. The tests based on the two statistics are extensions to the cross‐sectional dependence test and the bias‐adjusted Lagrange multiplier test. Unlike the two existing tests that are justified under sequential limits, the newly developed tests can be justified under simultaneous limits without any additional restriction imposed on the cross‐sectional and time‐series dimensions. Moreover, it is proved that the new tests can even be justified under high dimension, low sample size limits, provided that a homo‐rank condition holds. Several simulation experiments are conducted to evaluate the performance of the newly introduced tests. The simulation results show that use of the tests can bring significant improvement, especially in cases of large cross‐sectional dimension and small time‐series dimension.

show abstract

“…() and Lan et al. () have developed useful tests for H 0 under simultaneous limits. Without these restrictions, to test H 0 , Pesaran () proposed the CD test, and Pesaran et al.…”

Section: Model and Related Testsmentioning

confidence: 99%

Section: Tests Based On Pairwise Augmented Regressionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…(), the bias‐correction‐free LM test in Mao () and the AS test in Lan et al. () assume

N / T \to c

additionally as

(N, T) \to \infty

, where c is a fixed and finite positive number. Unlike these cases, the newly developed tests in the present paper do not need any restriction imposed on N and T .…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations